MIT News - Electrical engineering and computer science (EECS) - Computer science and technologyhttps://news.mit.edu/rss/topic/electrical-engineering-and-computer-science
MIT News is dedicated to communicating to the media and the public the news and achievements of the students, faculty, staff and the greater MIT community.enThu, 15 Nov 2018 23:59:59 -0500I think, therefore I codehttps://news.mit.edu/2018/student-jessy-lin-computer-science-philosophy-1116
Senior Jessy Lin, a double major in EECS and philosophy, is programming for social good.Thu, 15 Nov 2018 23:59:59 -0500Gina Vitale | MIT News correspondenthttps://news.mit.edu/2018/student-jessy-lin-computer-science-philosophy-1116<p>To most of us, a 3-D-printed turtle just looks like a turtle; four legs, patterned skin, and a shell. But if you show it to a particular computer in a certain way, that object’s not a turtle — it’s a gun.</p>
<p>Objects or images that can fool artificial intelligence like this are called adversarial examples. Jessy Lin, a senior double-majoring in computer science and electrical engineering and in philosophy, believes that they’re a serious problem, with the potential to trip up AI systems involved in driverless cars, facial recognition, or other applications. She and several other MIT students have formed a research group called LabSix, which creates examples of these AI adversaries in real-world settings — such as <a href="https://www.theguardian.com/technology/2017/nov/03/googles-ai-turtle-rifle-mit-research-artificial-intelligence">the turtle identified as a rifle</a> — to show that they are legitimate concerns.</p>
<p>Lin is also working on a project called Sajal, which is a system that could allow refugees to give their medical records to doctors via a QR code. This “mobile health passport” for refugees was born out of VHacks, a hackathon organized by the Vatican, where Lin worked with a team of people she’d met only a week before. The theme was to build something for social good — a guiding principle for Lin since her days as a hackathon-frequenting high school student.</p>
<p>“It’s kind of a value I’ve always had,” Lin says. “Trying to be thoughtful about, one, the impact that the technology that we put out into the world has, and, two, how to make the best use of our skills as computer scientists and engineers to do something good.”</p>
<p><strong>Clearer thinking through philosophy</strong></p>
<p>AI is one of Lin’s key interests in computer science, and she’s currently working in the Computational Cognitive Science group of Professor Josh Tenenbaum, which develops computational models of how humans and machines learn. The knowledge she’s gained through her other major, philosophy, relates more closely this work than it might seem, she says.</p>
<p>“There are a lot of ideas in [AI and language-learning] that tie into ideas from philosophy,” she says. “How the mind works, how we reason about things in the world, what concepts are. There are all these really interesting abstract ideas that I feel like … studying philosophy surprisingly has helped me think about better.”</p>
<p>Lin says she didn’t know a lot about philosophy coming into college. She liked the first class she took, during her first year, so she took another one, and another — before she knew it,&nbsp;she was hooked. It started out as a minor; this past spring, she declared it as a major.</p>
<p>“It helped me structure my thoughts about the world in general, and think more clearly about all kinds of things,” she says.</p>
<p>Through an interdisciplinary class on ethics and AI ethics, Lin realized the importance of incorporating perspectives from people who don’t work in computer science. Rather than writing those perspectives off, she wants to be someone inside the tech field who considers issues from a humanities perspective and listens to what people in other disciplines have to say.</p>
<p><strong>Teaching computers to talk</strong></p>
<p>Computers don’t learn languages the way that humans do — at least, not yet. Through her work in the Tenenbaum lab, Lin is trying to change that.</p>
<p>According to one hypothesis, when humans hear words, we figure out what they are by first saying them to ourselves in our heads. Some computer models aim to recreate this process, including recapitulating the individual sounds in a word. These “generative” models do capture some aspects of human language learning, but they have other drawbacks that make them impractical for use with real-world speech.</p>
<p>On the other hand, AI systems known as neural networks, which are trained on huge sets of data, have shown great success with speech recognition. Through several projects, Lin has been working on combining the strengths of both types of models, to better understand, for example, how children learn language even at a very young age.</p>
<p>Ultimately, Lin says, this line of research could contribute to the development of machines that can speak in a more flexible, human way.</p>
<p><strong>Hackathons and other pastimes</strong></p>
<p>Lin first discovered her passion for computer science at Great Neck North High School on Long Island, New York, where she loved staying up all night to create computer programs during hackathons. (More recently, Lin has played a key role in HackMIT, one of the Institute’s flagship hackathons. Among other activities, she helped organize the event from 2015 to 2017, and in 2016 was the director of corporate relations and sponsorship.) It was also during high school that she began to attend MIT Splash, a program hosted on campus offering a variety of classes for K-12 students.</p>
<p>“I was one of those people that always had this dream to come to MIT,” she says.</p>
<p>Lin says her parents and her two sisters have played a big role in supporting those dreams. However, her knack for artificial intelligence doesn’t seem to be genetic.</p>
<p>“My mom has her own business, and my dad is a lawyer, so … who knows where computer science came out of that?” she says, laughing.</p>
<p>In recent years, Lin has put her computer science skills to use in a variety of ways. While in high school, she interned at both New York University and Columbia University. During Independent Activities Period in 2018, she worked on security for Fidex, a friend’s cryptocurrency exchange startup. The following summer she interned at Google Research NYC on the natural language understanding team, where she worked on developing memory mechanisms that allow a machine to have a longer-term memory. For instance, a system would remember not only the last few phrases it read in a book, but a character from several chapters back. Lin now serves as a campus ambassador for Sequoia Capital, supporting entrepreneurship on campus.</p>
<p>She currently lives in East Campus, where she enjoys the “very vibrant dorm culture.” Students there organize building projects for each first-year orientation —&nbsp;when Lin arrived, they built a roller coaster. She’s helped with the building in the years since, including a geodesic dome that was taller than she is. Outside of class and building projects, she also enjoys photography.</p>
<p>Ultimately, Lin’s goal is to use her computer science skills to benefit the world. About her future after MIT, she says, “I think it could look something like trying to figure out how we can design AI that is increasingly intelligent but interacts with humans better.”</p>
Jessy Lin, an MIT senior double-majoring in electrical engineering and computer science and in philosophy.Image: Bryce Vickmarkstudent, Undergraduate, Profile, Electrical Engineering & Computer Science (eecs), Philosophy, School of Engineering, School of Humanities Arts and Social Sciences, Technology and society, Humanities, Computer science and technology, Machine learning, Artificial intelligence, Algorithms, Language, Brain and cognitive scienceArtificial intelligence summit adresses impact of technology on jobs and global economyhttps://news.mit.edu/2018/mit-ai-summit-addresses-tech-impact-on-jobs-global-economy-1115
Speakers at the summit included Massachusetts Secretary of Labor Rosalin Acosta and former Google chairman Eric Schmidt.Thu, 15 Nov 2018 13:50:00 -0500Adam Conner-Simons | MIT CSAILhttps://news.mit.edu/2018/mit-ai-summit-addresses-tech-impact-on-jobs-global-economy-1115<p>This week MIT hosted its second annual summit on “AI and the Future of Work,”&nbsp;bringing together representatives from industry, government and academia to discuss the opportunities and challenges of artificial intelligence (AI) and automation.</p>
<p>Co-hosted by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Initiative on the Digital Economy (IDE), the event featured former Alphabet chairman Eric Schmidt and Massachusetts Secretary of Labor Rosalin Acosta, who delivered the keynote address.</p>
<p>A common theme throughout the event was the importance of doing more than just thinking about technological disruption and actually working to create public policy that encourages the thoughtful deployment of AI systems.</p>
<p>“The technologies themselves are neutral, so the question is how to organize ourselves in society in a way that addresses their potential to change the job market,” said Diana Farrell, CEO of the JPMorgan Chase Institute. “We’re kidding ourselves if we think that the market is going to, on its own, allow these technologies to infiltrate and yield the kind of outcomes from society that we want.”</p>
<p>The focus on public policy also extended to education. Many panelists spoke of the importance of lifelong learning, in the form of a burgeoning industry of free and low-cost online classes to pick up skills in fields like machine learning and data science that have seen major job growth.</p>
<p>Some speakers believed that future focus needs to happen much earlier the educational pipeline. Fred Goff, who serves as CEO of the blue-collar job-search network Jobcase, did a survey of the platform’s 90 million members about their education. Half said that their K-12 background prepared them for their job today, but less than a quarter said that they think their education will prepare them for the jobs of tomorrow.</p>
<p>Beyond the U.S., industry analysts spoke about the importance of considering AI in the context of the developing world, where there is often low digital literacy.</p>
<p>“How do we support people in remote and isolated areas so that they don’t fall further behind?” asked Tina George, an expert in global technologies for the World Bank. “We can't build Star Wars with Flintstone technology.”</p>
<p>There was also a growing recognition that in industry, AI could actually become something of an equalizer, especially in areas like mergers and acquisitions that rely heavily on data analysis.</p>
<p>"It no longer requires a multi-million dollar budget to get AI going in your company," said Nichole Jordan, a managing partner at Grant Thornton LLP. “It represents an opportunity to level the playing field for smaller companies.”</p>
<p>On the academic side, CSAIL Director Professor Daniela Rus discussed the many ways that scientists are using AI for everything from diagnosing disease to predicting food shortages. At the same time, she talked about how important it is for researchers to be thoughtful and intentional as they work on these new breakthroughs.</p>
<p>“AI should be able to help us all get lifted to better lives, and I think there is a lot of potential still untapped,” Rus said in video remarks. “But we can't just push technology forward and hope for the best. We have to work to ensure that the best happens.”</p>
The keynote address at the second annual AI and the Future of Work summit was delivered by Rosalin Acosta, Massachusetts Secretary of Labor and Workforce Development.Photo: Rachel Gordon/CSAILSpecial events and guest speakers, Artificial intelligence, Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Technology and society, Autonomous vehicles, School of Engineering, Jobs, Industry, Health care, Data, Innovation and Entrepreneurship (I&E), Sloan School of ManagementPutting food-safety detection in the hands of consumershttps://news.mit.edu/2018/food-safety-rfid-detection-consumers-1114
Simple, scalable wireless system uses the RFID tags on billions of products to sense contamination.Wed, 14 Nov 2018 11:37:54 -0500Rob Matheson | MIT News Officehttps://news.mit.edu/2018/food-safety-rfid-detection-consumers-1114<p>MIT Media Lab researchers have developed a wireless system that leverages the cheap RFID tags already on hundreds of billions of products to sense potential food contamination — with no hardware modifications needed. With the simple, scalable system, the researchers hope to bring food-safety detection to the general public.</p>
<p>Food safety incidents have made headlines around the globe for causing illness and death nearly every year for the past two decades. Back in 2008, for instance, 50,000 babies in China were hospitalized after eating infant formula adulterated with melamine,&nbsp;an organic compound used to make plastics, which is toxic in high concentrations. And this April, more than 100 people in Indonesia died from drinking alcohol contaminated, in part, with methanol, a toxic alcohol commonly used to dilute liquor for sale in black markets around the world.</p>
<p>The researchers’ system, called RFIQ, includes a reader that senses minute changes in wireless signals emitted from RFID tags when the signals interact with food. For this study they focused on baby formula and alcohol, but in the future, consumers might have their own reader and software to conduct food-safety sensing before buying virtually any product. Systems could also be implemented in supermarket back rooms or in smart fridges to continuously ping an RFID tag to automatically detect food spoilage, the researchers say.</p>
<p>The technology hinges on the fact that certain changes in the signals emitted from an RFID tag correspond to levels of certain contaminants within that product. A machine-learning model “learns” those correlations and, given a new material, can predict if the material is pure or tainted, and at what concentration. In experiments, the system detected baby formula laced with melamine with 96 percent accuracy, and alcohol diluted with methanol with 97 percent accuracy.</p>
<p>“In recent years, there have been so many hazards related to food and drinks we could have avoided if we all had tools to sense food quality and safety ourselves,” says Fadel Adib, an assistant professor at the Media Lab who is co-author on a paper describing the system, which is being presented at the ACM Workshop on Hot Topics in Networks. “We want to democratize food quality and safety, and bring it to the hands of everyone.”</p>
<p>The paper’s co-authors include: postdoc and first author Unsoo Ha, postdoc Yunfei Ma, visiting researcher Zexuan Zhong, and electrical engineering and computer science graduate student Tzu-Ming Hsu.</p>
<div class="cms-placeholder-content-video"></div>
<p><strong>The power of “weak coupling”</strong></p>
<p>Other sensors have also been developed for detecting chemicals or spoilage in food. But those are highly specialized systems, where the sensor is coated with chemicals and trained to detect specific contaminations. The Media Lab researchers instead aim for broader sensing. “We’ve moved this detection purely to the computation side, where you’re going to use the same very cheap sensor for products as varied as alcohol and baby formula,” Adib says.</p>
<p>RFID tags are stickers with tiny, ultra-high-frequency antennas. They come on food products and other items, and each costs around three to five cents. Traditionally, a wireless device called a reader pings the tag, which powers up and emits a unique signal containing information about the product it’s stuck to.</p>
<p>The researchers’ system leverages the fact that, when RFID tags power up, the small electromagnetic waves they emit travel into and are distorted by the molecules and ions of the contents in the container. This process is known as “weak coupling.” Essentially, if the material’s property changes, so do the signal properties.</p>
<p>A simple example of feature distortion is with a container of air versus water. If a container is empty, the RFID will always respond at around 950 megahertz. If it’s filled with water, the water absorbs some of the frequency, and its main response is around only 720 megahertz. Feature distortions get far more fine-grained with different materials and different contaminants. “That kind of information can be used to classify materials … [and] show different characteristics between impure and pure materials,” Ha says.</p>
<p>In the researchers’ system, a reader emits a wireless signal that powers the RFID tag on a food container. Electromagnetic waves penetrate the material inside the container and return to the reader with distorted amplitude (strength of signal) and phase (angle).</p>
<p>When the reader extracts the signal features, it sends those data to a machine-learning model on a separate computer. In training, the researchers tell the model which feature changes correspond to pure or impure materials. For this study, they used pure alcohol and alcohol tainted with 25, 50, 75, and 100 percent methanol; baby formula was adulterated with a varied percentage of melamine, from 0 to 30 percent.</p>
<p>“Then, the model will automatically learn which frequencies are most impacted by this type of impurity at this level of percentage,” Adib says. “Once we get a new sample, say, 20 percent methanol, the model extracts [the features] and weights them, and tells you, ‘I think with high accuracy that this is alcohol with 20 percent methanol.’”</p>
<p><strong>Broadening the frequencies</strong></p>
<p>The system’s concept derives from a technique called radio frequency spectroscopy, which excites a material with electromagnetic waves over a wide frequency and measures the various interactions to determine the material’s makeup.</p>
<p>But there was one major challenge in adapting this technique for the system: RFID tags only power up at a very tight bandwidth wavering around 950 megahertz. Extracting signals in that limited bandwidth wouldn’t net any useful information.</p>
<p>The researchers built on a sensing technique they developed earlier, called two-frequency excitation, which sends two frequencies — one for activation, and one for sensing — to measure hundreds more frequencies. The reader sends a signal at around 950 megahertz to power the RFID tag. When it activates, the reader sends another frequency that sweeps a range of frequencies from around 400 to 800 megahertz. It detects the feature changes across all these frequencies and feeds them to the reader.</p>
<p>“Given this response, it’s almost as if we have transformed cheap RFIDs into tiny radio frequency spectroscopes,” Adib says.</p>
<p>Because the shape of the container and other environmental aspects can affect the signal, the researchers are currently working on ensuring the system can account for those variables. They are also seeking to expand the system’s capabilities to detect many different contaminants in many different materials.</p>
<p>“We want to generalize to any environment,” Adib says. “That requires us to be very robust, because you want to learn to extract the right signals and to eliminate the impact of the environment from what’s inside the material.”</p>
MIT Media Lab researchers have developed a wireless system that leverages the cheap RFID tags already on hundreds of billions of products to sense potential food contamination.Image courtesy of the researchers, edited by MIT NewsResearch, Computer science and technology, Algorithms, Media Lab, Wireless, Sensors, Supply chains, Food, Water, Health, Electrical Engineering & Computer Science (eecs), School of Engineering, School of Architecture and PlanningLooking back at Project Athena https://news.mit.edu/2018/mit-looking-back-project-athena-distributed-computing-for-students-1111
A revolutionary educational project in the 1980s put the tools of computation in students’ hands — and foreshadowed even greater changes.
Sun, 11 Nov 2018 00:02:00 -0500Eva Charles Anna Frederick | School of Engineeringhttps://news.mit.edu/2018/mit-looking-back-project-athena-distributed-computing-for-students-1111<p>In October, the Institute announced the creation of&nbsp;the MIT Stephen A. Schwarzman College of Computing, an ambitious new enterprise that will allow students to better tailor their educational interests to their goals. But the ideas driving this exciting new effort may carry a distant echo —&nbsp;especially among alumni who were at MIT during the 1980s —&nbsp;from the&nbsp;time&nbsp;leadership launched&nbsp;another computing enterprise that dramatically changed how undergraduates and graduate students learned.</p>
<p>Project Athena was a campus-wide effort to make the tools of computing available to every discipline at the Institute and provide students with systematic access to computers. A new project that featured computer workstations and educational programming, Athena was a milestone in the history of distributed systems and inspired programs like Kerberos. It also revolutionized educational computing for the Institute and beyond, and created the computing environment that many students and faculty still work in today.</p>
<p>“Before we had [Athena], our students complained about the lack of computing in such a technology-centered institution,” says Joel Moses, an Institute Professor at MIT and one of the initial leaders of Project Athena. “Athena turned MIT into one of the most computer-rich institutions in the country.”</p>
<p>“The founders of of project Athena believed that computation should be used broadly by a lot of people for a lot of reasons,” says Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science (EECS) and director of MIT’s Computer Science and Artificial Intelligence Laboratory.</p>
<p>“They set out to create an education environment to empower MIT students to do just that. Since then, the MIT faculty and students have left their fingerprints all over the biggest accomplishments in the field of computing from systems to theory to artificial intelligence,” she says.</p>
<p>In 1983, the year Project Athena began, it was still possible for students to receive a science or engineering degree from MIT without ever having touched a computer. That was despite&nbsp;digital computers having&nbsp;been on campus since 1947, when the Navy commissioned Whirlwind I, one of the world’s first real-time computers. (It was powered by vacuum tubes.) But at the time, computers were nearly all provided by research funds which restricted their use.</p>
<p>Pre-Athena, MIT students who needed to use computers could work on computing systems such as CTSS. These systems did have some drawbacks, though. For one thing, students often had to wait in line at all hours of the day to do their work. In 1969, the Institute moved from CTSS to MULTICS, which was supported primarily by research funds with limited access for educational purposes. It included a timeshare aspect which meant that if students went over their allotted time, they weren’t allowed to run any more programs until the timeshare refreshed.</p>
<p>“(Before Athena), there was no internet access or email, no way to share files, and no standard anything. There was no @mit.edu address,” says Earll Murman, the director of the latter half of the eight-year project. “Athena changed all of that.”</p>
<p>Even when personal computers first started to appear on campus in the mid to late 1980s, they were still too expensive for many students. For about $1,000 a consumer could buy a computer with a 5 MB hard drive — which today is about enough space to store an MP3 of Bonnie Tyler's “Total Eclipse of the Heart,”&nbsp;the song that topped of the charts in Athena’s first year.</p>
<p><strong>Getting going</strong></p>
<p>The leadership at MIT knew that as a technology-centered school, MIT needed to incorporate more computing into their education. So, in May 1983, under the care of a committee of faculty from the Department of Electrical Engineering and Computer Science — including then-EECS head Moses;&nbsp;Michael Dertouzos, the director of the Laboratory for Computer Science (now CSAIL);&nbsp;and Dean of the School of Engineering Gerald Wilson — the largest educational project ever undertaken at MIT was launched at the eventual cost of around $100 million. The project was&nbsp;largely paid for with&nbsp;funding from the Digital Equipment Corporation (DEC) and IBM.</p>
<p>The leaders of the project named it “Project Athena” after the ancient Greek goddess of wisdom, war, and the crafts. Unlike her namesake, however, Athena did not spring fully formed and outfitted with programs from the head of her creators. When the project started, it was ambitious and a little vague. Goals spanned from creating a cohesive network of compatible computers, to establishing a knowledge base for future decisions in educational computing, to helping students share information and learn more fully across all disciplines.</p>
<p>To supply the system with some clarity and direction, the committee went to the faculty and asked them to develop their own software for use in their classes and for students to work on. Projects — there were 125 in total — ranged from aerospace engineering simulations to language-learning applications to biology modeling tools.</p>
<p>Athena took off.</p>
<p>“I felt that we would know Athena was successful if we were surprised by some of the applications,” Moses says. “It turned out that our surprises were largely in the humanities.”</p>
<p>One such surprise was the Athena Writing Project, spearheaded by MIT professors James Paradis and Ed Barrett, which aimed to create an online classroom system for teaching scientific writing. The system allowed students to edit and annotate papers, present classwork, and turn in assignments.</p>
<p><strong>The hardware</strong></p>
<p>Of course, in order for students to be able to use all the educational programming, there had to be enough terminals for them to access the system. That’s where Professor Jerome Saltzer came in. While much of the leadership of the project was focused on overseeing the faculty proposals and research, Saltzer stepped in as the technical director of the project in 1983 and led the effort to bring the physical workstations, made by IBM, to all students.</p>
<p>Luckily for Saltzer and MIT, from its inception and beyond, Project Athena was on the cutting edge of distributed systems computing at the time. The Institute found a range of willing partners in industry, such as IBM and DEC, that were willing to provide MIT with funding, technology and hardware.</p>
<p>Project Athena formally ended in 1991. By then the project (and computing in general) had become much more pervasive and commonplace in MIT students’ lives. There were hundreds of Athena workstations located in clusters around the campus, and students were using them to measure bloodflow, design airplane wings, practice political science debates, digital revise their humanities papers, and hundreds of other things.</p>
<p><strong>Athena’s wisdom today</strong></p>
<p>It has now been 27 years since Project Athena ended, but the Athena computing environment is still a part of everyday life at MIT. There are Athena clusters located around campus, with many workstations hooked up to printers and available to students 24 hours a day, seven days a week (although there are fewer workstations than there once were, and they are typically used for more specialized applications).&nbsp;</p>
<p>Though Project Athena’s main goals were educational, it had long-lasting effects on a range of technologies, products, and services that the MIT community touches every day, often without even knowing it. Athena’s impact can be&nbsp;seen in the integration of third-party software like Matlab into education. Its use of overlapping windows — students could be watching videos in one window, chatting with friends and classmates in another, and working on homework in a third — was the start of the X Window system, which is now common on Unix displays. Athena also led to the development of the Kerberos authentication system (named, in keeping with the Greek mythology motif, after the three-headed dog which guards the Underworld) which is now used at institutions around the world.</p>
<p>For Drew Houston ’05, Athena was a source of inspiration.</p>
<p>“With Athena, you could sit down at any of the (hundreds) of workstations on campus and your whole environment followed you around — not only your files,” he says. “When I graduated, not only did I not have that anymore, but it felt like for most people they didn't have anything like that, so I certainly saw a big opportunity to deliver that kind of experience to a much larger audience.”</p>
<p>The result was Dropbox, which Houston and his co-founder launched in 2008, allowing users to access their files from any synced device. “When we recruited engineers, part of our pitch was we were trying to build Athena for the rest of the world,” Houston says.</p>
<p>As MIT moves forward with the new college, Vice Chancellor Ian Waitz sees a parallel between the college and Project Athena. Like the new college, Athena was a way to change the structure of MIT’s education and provide a platform for students to create and problem-solve.</p>
<p>“One of the things that we do here is try to provide resources for people to use, and they may even use them in ways that we don't imagine,” Waitz says. “That’s a pretty broad analogy to a lot of the stuff that we do here at MIT — we bring bright people together and give them the tools and problems to solve, and they’ll go off and do it.”</p>
<p>“Computers have made our daily lives easier in a million ways that people don’t even notice, from online shopping to digital cameras, from antilock brakes to electronic health records, and everything in between,” adds Rus.<br />
&nbsp;<br />
“Computing helps us with all the little things and it is also vital to the really big things like traveling to the stars, sequencing the human genome, making our food, medicines, and lives safer,” she says. “Through the MIT Schwarzman College of Computing we will create the education and research environment to make computing a stronger tool and find new ways to apply it.”</p>
From 1983 to 1991, MIT partnered with IBM and the Digital Equipment Corporation to provide computer workstations for students working on aerospace engineering, language learning, educational projects, and much more. Photo courtesy of the MIT MuseumSchool of Engineering, MIT Schwarzman College of Computing, Department of Electrical Engineering and Computer Science (EECS), Computer Science and Artificial Intelligence Laboratory (CSAIL), Campus services, Computer science and technology, Information Systems and Technology, History of MITBridge to the future of engineeringhttps://news.mit.edu/2018/bridge-to-the-future-of-engineering-1111
The School of Engineering’s faculty leadership weigh in on what the MIT Stephen A. Schwarzman College of Computing will mean for their students and faculty.
Sun, 11 Nov 2018 00:01:00 -0500https://news.mit.edu/2018/bridge-to-the-future-of-engineering-1111<p>School of Engineering faculty are embracing the new <a href="http://news.mit.edu/2018/letter-mit-community-regarding-mit-stephen-schwarzman-college-computing">MIT Stephen A. Schwarzman College of Computing</a> as a bold response to the rapid evolution of computing that is altering and, in many cases, fundamentally transforming their disciplines.</p>
<p>Inspired by student interest in computing, MIT President L. Rafael Reif launched an assessment process more than a year ago that involved widespread engagement with key stakeholders across the MIT community. Discussions were led by President Reif, Provost Martin A. Schmidt, and Dean of the School of Engineering Anantha P. Chandrakasan with Faculty Chair Susan Silbey playing a key role.</p>
<p>“The creation of the college is MIT’s first major academic structural change since 1950,” says Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science. “After consulting with faculty from across engineering and throughout MIT, the need to do something timely and deeply impactful was abundantly clear. Mr. Schwarzman’s inspired and amazingly generous support was instrumental to our ability to move forward.”</p>
<p>The school’s eight department heads and two institute directors recently spoke of the exciting possibilities ahead as the college, which represents a $1 billion commitment, gets underway. There will be a new building, a new dean, and 50 new faculty positions located within the college and jointly with other departments across MIT.</p>
<p>School leadership says the college meets a significant need partly because it directly aligns with recent activities and changes in some of their own practices. For example, many departments have adapted their hiring and recruitment practices to include a heavier emphasis on selecting faculty who can work at a high level in computation along with another specialized field, says Chandrakasan. “In some ways the change has arrived,” he says. “The college is our way of building a powerful framework and environment for research and collaborations that involve computing and that are occurring across disciplines. The college remains a young idea and its vibrancy and success will depend on thoughtful input from people across MIT, which I look forward to hearing.”</p>
<p><strong>At the forefront</strong></p>
<p>The eye of the storm of change has undoubtedly been in the Department of Electrical Engineering and Computer Science (EECS). Faculty do research to advance core computing topics while also addressing an inundation of requests to build bridges and connect their work with other disciplines. In the last two years alone, EECS faculty have established new joint academic programs with economics and urban science and planning.</p>
<p>The creation of the college will provide vital support and accelerate all kinds of computing-related research and learning that is happening across the Institute, says Asu Ozdaglar, School of Engineering Distinguished Professor of Engineering and EECS department head. “With the launch of the college, we hope that MIT’s leading position in research and the education of future leaders in computing will continue and grow.”</p>
<p>Markus Buehler, head of the Department of Civil and Environmental Engineering and the McAfee Professor of Engineering, agrees. “We have been at the forefront of this transformation of our discipline,” he says. The increased role of computing has impacted all five of CEE’s strategic focus areas, which include ecological systems, resources, structures and design, urban systems, and global systems. As a result, the department is now planning a potential new major between CEE and computer science, and the college will help in that effort, says Buehler. “The creation of the college will serve as a key enabler,” he says.</p>
<p>The MIT Institute for Data, Systems, and Society is also deeply aligned with the college, says Munther Dahleh, director of IDSS and the William A. Coolidge Professor of Electrical Engineering and Computer Science. IDSS works with all five schools to promote cross-cutting education and research to advance data science and information and decision systems in order to address societal challenges in a systematic and rigorous manner. IDSS plays a “bridge” role that will prove useful to the college, Dahleh says. It has launched cross-disciplinary academic programs, hired joint faculty in three schools, and enabled collaborations across all five schools.</p>
<p>“The new college will provide a structure for expanding these activities, he says. “And it will create new opportunities to connect with a larger community in sciences, social science, and urban planning and architecture.”</p>
<p><strong>Steeped in computing</strong></p>
<p>The timing is right for the college, say the faculty. “We are excited by the growth opportunities in computing because the nuclear science and engineering disciplines are so steeped in the development and application of numerical tools,” says Dennis Whyte, the Hitachi America Professor of Engineering and head of the Department of Nuclear Science and Engineering.</p>
<p>The Department of Aeronautics and Astronautics has&nbsp;a significant number of faculty working in information engineering for aerospace systems, particularly autonomous systems, says Daniel Hastings, the Cecil and Ida Green Education Professor at MIT and incoming head of the department.</p>
<p>“The college will allow us to expand our research and teaching into all the ways that computing technologies are changing the aerospace enterprise,” says Hastings. Those ways include deep learning to recognize patterns for maintenance in the operation of multiple aircraft, artificial intelligence for traffic control of fleets of uninhabited flying vehicles, and intelligent robotic systems in space to service low-Earth orbit satellites, among others.</p>
<p>Increasingly, the tools of machine learning and artificial intelligence are being fruitfully applied to materials design problems, says Christopher A. Schuh, the Danae and Vasilis Salapatas Professor of Metallurgy and head of the Department of Materials Science and Engineering (DMSE). “Our department sees computational thinking as a critical skill set for any budding materials scientist,” he says, adding a large fraction of DMSE faculty focus on computational materials science or use computational methods in designing new materials.</p>
<p>“We are excited to see MIT focusing on computing broadly, and we look forward to a deep materials-centric engagement with the college,” he says.</p>
<p><strong>Growth opportunities </strong></p>
<p>Paula Hammond, the David H. Koch Professor in Engineering and head of the Department of Chemical Engineering, would like to see the college provide new opportunities and pathways for chemical engineering to grow. One-third of faculty in her department work with computation as their primary research method, she says.</p>
<p>Hammond looks forward especially to the arrival of new faculty. “I see these new positions as a chance to hire faculty members who are rooted in the molecular and systems-oriented thinking that defines our field, while doing research in new and important areas, including global problems in environment, energy, health, and water.” She says such interdisciplinary faculty would be instrumental in building a new computational major in chemical engineering (10-ENG) that is currently in development.</p>
<p>Douglas Lauffenburger, the Ford Professor of Bioengineering and head of MIT’s Department of Biological Engineering, expresses a similar hope. “The creation of the college is a bold step, and I'm hopeful that some of these additional faculty positions will enable a strengthening of computational biology on campus.”</p>
<p><strong>Training the next generation</strong></p>
<p>Faculty also spoke of how the college will enable MIT students to play leadership roles in the future of computing — and other engineering fields. “It will strengthen our ability to train the next generation of mechanical engineers and better prepare students to join the workforce by exposing them to computation and AI throughout their education,” says Evelyn N. Wang, the Gail E. Kendall Professor and head of the Department of Mechanical Engineering.</p>
<p>An increasing number of research fields within mechanical engineering rely on computing technologies — from smarter autonomous machines to more accurate extreme event prediction and -3D printing. “The college will help students and researchers working in these fields advance their groundbreaking research even further,” adds Wang.</p>
<p>Elazer Edelman, the director of the Institute for Medical Engineering and Science, says the potential is vast. “From access to critical data sets to insights derived from machine and deep learning, the college will enable all of us to better interact as a community to address important problems and to train the next batch of young stars at the interface of science, engineering, computing and medicine,” he says. Edelman is the Edward J. Poitras Professor of Medical Engineering and Science at MIT.</p>
<p>“We at IMES are particularly excited to work with the college in interacting as a global community of scholars from this incredibly exciting and imaginative platform,” he says.</p>
“The college is our way of building a powerful framework and environment for research and collaborations that involve computing and that are occurring across disciplines," says School of Engineering Dean Anantha P. Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science.Image: Christopher Harting and Lesley Rock MIT Schwarzman College of Computing, School of Engineering, Algorithms, Artificial intelligence, Biological engineering, Aeronautical and astronautical engineering, Chemical engineering, Civil and environmental engineering, Computer science and technology, Electrical Engineering & Computer Science (eecs), Institute for Medical Engineering and Science (IMES), Machine learning, Materials Science and Engineering, DMSE, Mechanical engineering, Nuclear science and engineering, IDSS, President L. Rafael Reif, FacultyHighlighting new research opportunities in civil and environmental engineeringhttps://news.mit.edu/2018/mit-faculty-discuss-research-opportunities-in-cee-1107
At its annual alumni reception, CEE faculty shared innovative research projects ranging from machine learning to regional impacts of climate change.Wed, 07 Nov 2018 10:50:00 -0500Taylor De Leon | Department of Civil and Environmental Engineeringhttps://news.mit.edu/2018/mit-faculty-discuss-research-opportunities-in-cee-1107<p>On October 18, alumni, postdocs, graduate students, and professors gathered at the Department of Civil and Environmental Engineering’s annual New Research Alumni Reception.&nbsp;The annual event invites the broader MIT CEE community back to campus to network with peers and become informed about the innovative&nbsp;research projects that the&nbsp;civil and environmental engineering faculty are working on.</p>
<p>Topics covered ranged from machine learning and blockchain to regional impacts of global climate change, and creative computing for high performance design in structural design.&nbsp;</p>
<p>McAfee Professor of Engineering and department head Markus Buehler began the night by discussing new additions to CEE and MIT such as&nbsp;the grand opening of MIT.nano,&nbsp;the new MIT Schwarzman College of Computing to support the rise of artificial intelligence and data science, and a revamped student lounge named&nbsp;in honor of late professor and former department head Joseph Sussman.&nbsp;</p>
<p>Buehler also highlighted the fieldwork offered in CEE, such as ONE-MA3 (Materials in Art, Archeology and Architecture) in Italy; TREX (Traveling Research Environmental Experiences) in Hawaii; and the Agricultural Microbial Ecology subject led by Professor Otto Cordero during January Independent Activities Period in Israel. Furthermore, Buehler explained that CEE provides students with resources such as the new Internship program, alumni panels and receptions, and career fairs, which all offer students a competitive edge post-graduation.&nbsp;</p>
<p>“The future presents vast, global challenges. MIT is rising to meet them with a wide lens and broad scale work,”&nbsp;Buehler said.</p>
<p><strong>Changing faces of computation</strong></p>
<p>With MIT’s recent $1 billion investment going toward the new MIT Schwarzman College of Computing, Professor John Williams appropriately presented on “New Faces of Computation” — machine learning&nbsp;and blockchain —&nbsp;that are already impacting various aspects of our lives.&nbsp;</p>
<p>“We can now build machines that can learn to do things that we as humans can’t do,” Williams states. In other words, as noted previously by MIT President L. Rafael Reif, “in order to partner with these machines, we will all need to be bilingual.”</p>
<p>Williams also discussed the importance of MIT’s Geospatial Data Center (GDC), which focuses on computation research in data science, cloud computing, cybersecurity, augmented reality, the internet of things, blockchain, and educational technology. Williams and GDC research scientist Abel Sanchez, a CEE research scientist, are introducing a new two-month online course called Digital Transformation, which focuses on understanding the technologies driving radical changes in industry. The course provides an MIT certificate issued by the Professional Education Program, upon satisfactory completion.&nbsp;</p>
<p>The world has a vast amount of data that is constantly growing due to our devices, which are able to track everything and everyone. “The amount of data that we are generating has given rise to news innovations in machine learning,”&nbsp;Williams said. “We have big data that is now being leveraged by companies such as Facebook and Google.”</p>
<p>Williams explained that we are capable of creating smart cities by utilizing tools that can be applied to tracking people and transportation, producing detailed maps, conducting digital and high precision farming, and more. He added that the speed of our world is going to increase considerably because machines are capable of learning and outperforming humans.</p>
<p><strong>Regional impacts of global climate change</strong></p>
<p>While machine learning and big data are on the rise, so are the carbon dioxide&nbsp;emissions produced by human activity. Breene M. Kerr Professor Elfatih Eltahir discussed misconceptions of climate change, his research on the rise in global temperatures, and the relationship between climate and infectious diseases.&nbsp;</p>
<p>He explained that many people in America are aware that global warming exists, however, they do not believe it will directly influence their lives. Eltahir urged&nbsp;a shift in mindset, noting that we could experience very significant effects of global warming.&nbsp;</p>
<p>“What my group and I look at is how to translate the impacts of climate change into areas such as health, agriculture, water resources; focusing on what people care about at local and regional scales,” Eltahir explained.</p>
<p>Eltahir emphasized that alarming temperatures will be seen around the Persian Gulf, South Asia, and Eastern China, with some areas being uninhabitable during the summers. Eltahir and various CEE students collaborated in the creation of the MIT regional climate model (MRCM), as well as the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS) in order to predict regional impacts of climate change.&nbsp;</p>
<p>Eltahir’s research also examines the relationship between climate and the spread of malaria, showing that there is a small window of temperature that mosquitoes can survive. Although climate change presents many challenges, it could also allow for a decrease in malaria transmission in parts of Africa.&nbsp;</p>
<p><strong>Creative computing for high performance design in structural engineering&nbsp;</strong></p>
<p>Integrating computation and climate with architecture, associate professor in CEE and architecture Caitlin Mueller explained how the utilization of machine learning and optimization tools early on in the design process can improve performance and merge structural engineering and design.&nbsp;</p>
<p>“I think performance, engineering, and efficiency can lead us to some of the most exciting design outcomes out there,” Mueller said.&nbsp;</p>
<p>Building off of&nbsp;Eltahir’s presentation, Mueller explained the impacts that construction has on the environment. According to Mueller, in the built environment, buildings use roughly 40 percent&nbsp;of energy worldwide and produce a similar proportion of global emissions. She stressed that the environment has not been part of the conversation enough in terms of design, but is hopeful that advancements in computation can help identify these impacts early on.&nbsp;</p>
<p>Mueller’s research lab, Digital Structures, focuses on linking architectural design and structural engineering through early tools or computational design, in order to help improve performance, energy efficiency, design, and cost.&nbsp;</p>
<p>In one example, Mueller developed a sofware called structureFIT, which helps designers create shapes for trusses&nbsp;and allows for human and computer collaboration. The software includes an interactive evolutionary algorithm where the computer suggests diverse and high-performing designs to the user.&nbsp;</p>
<p>With&nbsp;structureFIT&nbsp;and subsequent tools from Mueller’s group, the user is able to analyze the information about the performance of each design much more efficiently. Designers sort through various considerations such as how much material would be necessary, impact on the environment, and which designs are the most intriguing to them. However, the challenging part of this is that the computer generates an overwhelming amount of options.&nbsp;</p>
<p>Said Mueller:&nbsp;“There is huge opportunity to use advancements in data science and computation to start addressing this challenge we’ve created for ourselves.”</p>
<p>The machine clusters large data sets and organizes them into families of designs or typologies. Typology, a process that has always been manual, is now able to be sorted through the computer, providing the designer with instantaneous feedback. Another example of a machine learning application includes surrogate modelling, which uses real-time approximations for slow simulations to give designers real-time performance information in design processes.</p>
<p>With the help of robots that 3-D print extremely efficient and complex structures, Mueller is able to materialize these designs, while maintaining creativity and performance.&nbsp;</p>
<p>“Using this combination of state-of-the-art path planning algorithms with topology optimization, we can finally make these structures a reality,”&nbsp;Mueller said.&nbsp;</p>
<p>“We are investigating a lot of different topics, and collaborating with students in computer science,” she said.&nbsp;“What really unites us is that performance can absolutely play a critical role in creative design when we use these digital tools in creative ways.”&nbsp;&nbsp;</p>
<p>The alumni and community members present expressed enthusiasm&nbsp;about the new research projects coming from the department — from computation, to climate change and architecture, the&nbsp;innovative research was described as interrelated and highly impactful. After questions and discussion from the attendees, the evening concluded.&nbsp;</p>
<p>“The research our faculty conduct on a daily basis is inspiring,”&nbsp;said Buehler, reflecting&nbsp;on the night. “It is always gratifying when we have the opportunity to show alumni the innovative direction the department is moving in.”</p>
Associate Professor Caitlin Mueller presented on creative computing for high performance design in structural engineering.Photo: Kathleen BrianaSchool of Engineering, Civil and environmental engineering, Research, Alumni/ae, Special events and guest speakers, Computer science and technology, Machine learning, Climate change, Global Warming, Artificial intelligence, Faculty, MIT Schwarzman College of ComputingMachine-learning system could aid critical decisions in sepsis carehttps://news.mit.edu/2018/machine-learning-sepsis-care-1107
Model predicts whether ER patients suffering from sepsis urgently need a change in therapy.Wed, 07 Nov 2018 00:00:00 -0500Rob Matheson | MIT News Officehttps://news.mit.edu/2018/machine-learning-sepsis-care-1107<p>Researchers from MIT and Massachusetts General Hospital (MGH) have developed a predictive model that could guide clinicians in deciding when to give potentially life-saving drugs to patients being treated for sepsis in the emergency room.</p>
<p>Sepsis is one of the most frequent causes of admission, and one of the most common causes of death, in the intensive care unit. But the vast majority of these patients first come in through the ER. Treatment usually begins with antibiotics and intravenous fluids, a couple liters at a time. If patients don’t respond well, they may go into septic shock, where their blood pressure drops dangerously low and organs fail. Then it’s often off to the ICU, where clinicians may reduce or stop the fluids and begin vasopressor medications such as norepinephrine and dopamine, to raise and maintain the patient’s blood pressure.</p>
<p>That’s where things can get tricky. Administering fluids for too long may not be useful and could even cause organ damage, so early vasopressor intervention may be beneficial. In fact, early vasopressor administration has been linked to improved mortality in septic shock. On the other hand, administering vasopressors too early, or when not needed, carries its own negative health consequences, such as heart arrhythmias and cell damage. But there’s no clear-cut answer on when to make this transition; clinicians typically must closely monitor the patient’s blood pressure and other symptoms, and then make a judgment call.</p>
<p>In a paper being presented this week at the American Medical Informatics Association’s Annual Symposium, the MIT and MGH researchers describe a model that “learns” from health data on emergency-care sepsis patients and predicts whether a patient will need vasopressors within the next few hours. For the study, the researchers compiled the first-ever dataset of its kind for ER sepsis patients. In testing, the model could predict a need for a vasopressor more than 80 percent of the time.</p>
<p>Early prediction could, among other things, prevent an unnecessary ICU stay for a patient that doesn’t need vasopressors, or start early preparation for the ICU for a patient that does, the researchers say.</p>
<p>“It’s important to have good discriminating ability between who needs vasopressors and who doesn’t [in the ER],” says first author Varesh Prasad, a PhD student in the Harvard-MIT Program in Health Sciences and Technology. “We can predict within a couple of hours if a patient needs vasopressors. If, in that time, patients got three liters of IV fluid, that might be excessive. If we knew in advance those liters weren’t going to help anyway, they could have started on vasopressors earlier.”</p>
<p>In a clinical setting, the model could be implemented in a bedside monitor, for example, that tracks patients and sends alerts to clinicians in the often-hectic ER about when to start vasopressors and reduce fluids. “This model would be a vigilance or surveillance system working in the background,” says co-author Thomas Heldt, the W. M. Keck Career Development Professor in the MIT Institute of Medical Engineering and Science. “There are many cases of sepsis that [clinicians] clearly understand, or don’t need any support with. The patients might be so sick at initial presentation that the physicians know exactly what to do. But there’s also a ‘gray zone,’ where these kinds of tools become very important.”</p>
<p>Co-authors on the paper are James C. Lynch, an MIT graduate student; and Trent D. Gillingham, Saurav Nepal, Michael R. Filbin, and Andrew T. Reisner, all of MGH. Heldt is also an assistant professor of electrical and biomedical engineering in MIT’s Department of Electrical Engineering and Computer Science and a principal investigator in the Research Laboratory of Electronics.</p>
<p>Other models have been built to predict which patients are at risk for sepsis, or when to administer vasopressors, in ICUs. But this is the first model trained on the task for the ER, Heldt says. “[The ICU] is a later stage for most sepsis patients. The ER is the first point of patient contact, where you can make important decisions that can make a difference in outcome,” Heldt says.</p>
<p>The primary challenge has been a lack of an ER database. The researchers worked with MGH clinicians over several years to compile medical records of nearly 186,000 patients who were treated in the MGH emergency room from 2014 to 2016. Some patients in the dataset had received vasopressors within the first 48 hours of their hospital visit, while others hadn’t. Two researchers manually reviewed all records of patients with likely septic shock to include the exact time of vasopressor administration, and other annotations. (The average time from presentation of sepsis symptoms to vasopressor initiation was around six hours.)</p>
<p>The records were randomly split, with 70 percent used for training the model and 30 percent for testing it. In training, the model extracted up to 28 of 58 possible features from patients who needed or didn’t need vasopressors. Features included blood pressure, elapsed time from initial ER admission, total fluid volume administered, respiratory rate, mental status, oxygen saturation, and changes in cardiac stroke volume — how much blood the heart pumps in each beat.</p>
<p>In testing, the model analyzes many or all of those features in a new patient at set time intervals and looks for patterns indicative of a patient that ultimately needed vasopressors or didn’t. Based on that information, it makes a prediction, at each interval, about whether the patient will need a vasopressor. In predicting whether patients needed vasopressors in the next two or more hours, the model was correct 80 to 90 percent of the time, which could prevent an excessive half a liter or more of administered fluids, on average.</p>
<p>“The model basically takes a set of current vital signs, and a little bit of what the trajectory looks like, and determines that this current observation suggests this patient might need vasopressors, or this set of variables suggests this patient would not need them,” Prasad says.</p>
<p>Next, the researchers aim to expand the work to produce more tools that predict, in real-time, if ER patients may initially be at risk for sepsis or septic shock. “The idea is to integrate all these tools into one pipeline that will help manage care from when they first come into the ER,” Prasad says.</p>
<p>The idea is to help clinicians at emergency departments in major hospitals such as MGH, which sees about 110,000 patients annually, focus on the most at-risk populations for sepsis. “The problem with sepsis is the presentation of the patient often belies the seriousness of the underlying disease process,” Heldt says. “If someone comes in with weakness and doesn’t feel right, a little bit of fluids may often do the trick. But, in some cases, they have underlying sepsis and can deteriorate very quickly. We want to be able to tell which patients have become better and which are on a critical path if left untreated.”</p>
<p>The work was supported, in part, by a National Defense Science and Engineering Graduate Fellowship, the MIT-MGH Strategic Partnership, and by CRICO Risk Management Foundation and Nihon Kohden Corporation.</p>
A new machine-learning model that predicts whether ER patients suffering from sepsis may need to be switched to certain medications could help guide clinicians in sepsis care.MIT News OfficeResearch, Computer science and technology, Algorithms, Data, Health care, Machine learning, Medicine, Institute for Medical Engineering and Science (IMES), Research Laboratory of Electronics, Harvard-MIT Program in Health Sciences and Technology, Electrical Engineering & Computer Science (eecs), School of EngineeringWhy some Wikipedia disputes go unresolvedhttps://news.mit.edu/2018/wikipedia-disputes-unresolved-study-1106
Study identifies reasons for unsettled editing disagreements and offers predictive tools that could improve deliberation.Tue, 06 Nov 2018 12:17:23 -0500Rob Matheson | MIT News Officehttps://news.mit.edu/2018/wikipedia-disputes-unresolved-study-1106<p>Wikipedia has enabled large-scale, open collaboration on the internet’s largest general-reference resource. But, as with many collaborative writing projects, crafting the content can be a contentious subject.</p>
<p>Often, multiple Wikipedia editors will disagree on certain changes to articles or policies. One of the main ways to officially resolve such disputes is the Requests for Comment (RfC) process. Quarreling editors will publicize their deliberation on a forum, where other Wikipedia editors will chime in and a neutral editor will make a final decision.</p>
<p>Ideally, this should solve all issues. But a novel study by MIT researchers finds debilitating factors —&nbsp;such as excessive bickering and poorly worded arguments — have led to about one-third of RfCs going unresolved.</p>
<p>For the study, the researchers compiled and analyzed the first-ever comprehensive dataset of RfC conversations, captured over an eight-year period, and conducted interviews with editors who frequently close RfCs, to understand why they don’t find a resolution. They also developed a machine-learning model that leverages that dataset to predict when RfCs may go stale. And, they recommend digital tools that could make deliberation and resolution more effective.</p>
<p>“It was surprising to see a full third of the discussions were not closed,” says Amy X. Zhang, a PhD candidate in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-author on the paper, which is being presented at this week’s ACM Conference on Computer-Supported Cooperative Work and Social Computing. “On Wikipedia, everyone’s a volunteer. People are putting in the work, and they have interest … and editors may be waiting on someone to close so they can get back to editing. We know, looking through the discussions, the job of reading through and resolving a big deliberation is hard, especially with back and forth and contentiousness. [We hope to] help that person do that work.”</p>
<p>The paper’s co-authors are: first author Jane Im, a graduate student at the University of Michigan’s School of Information; Christopher J. Schilling of the Wikimedia Foundation; and David Karger, a professor of computer science and CSAIL researcher.</p>
<p><strong>(Not) finding closure</strong></p>
<p>Wikipedia offers several channels to solve editorial disputes, which involve two editors hashing out their problems, putting ideas to a simple majority vote from the community, or bringing the debate to a panel of moderators. Some previous Wikipedia research has delved into those channels and back-and-forth “edit wars” between contributors. “But RfCs are interesting, because there’s much less of a voting mentality,” Zhang says. “With other processes, at the end of day you’ll vote and see what happens. [RfC participants] do vote sometimes, but it’s more about finding a consensus. What’s important is what’s actually happening in a discussion.”</p>
<p>To file an RfC, an editor drafts a template proposal, based on a content dispute that wasn’t resolved in an article’s basic “talk” page, and invites comment by the broader community. Proposals run the gamut, from minor disagreements about a celebrity’s background information to changes to Wikipedia’s policies. Any editor can initiate an RfC and any editor —&nbsp;usually, more experienced ones — who didn’t participate in the discussion and is considered neutral, may close a discussion. After 30 days, a bot automatically removes the RfC template, with or without resolution. RfCs can close formally with a summary statement by the closer, informally due to overwhelming agreement by participants, or be left stale, meaning removed without resolution. &nbsp;</p>
<p>For their study, the researchers compiled a database consisting of about 7,000&nbsp;RfC conversations from the English-language Wikipedia from 2011 to 2017, which included closing statements, author account information, and general reply structure. They also conducted interviews with 10 of Wikipedia’s most frequent closers to better understand their motivations and considerations when resolving a dispute.</p>
<p>Analyzing the dataset, the researchers found that about 57 percent of RfCs were formally closed. Of the remaining 43 percent, 78 percent (or around 2,300) were left stale without informal resolution — or, about 33 percent of all the RfCs studied. Combining dataset analysis with the interviews, the researchers then fleshed out the major causes of resolution failure. Major issues include poorly articulated initial arguments, where the initiator is unclear about the issue or writes a deliberately biased proposal; excessive bickering during discussions that lead to more complicated, longer, argumentative threads that are difficult to fully examine; and simple lack of interest from third-party editors because topics may be too esoteric, among other factors.</p>
<p><strong>Helpful tools</strong></p>
<p>The team then developed a machine-learning model to predict whether a given&nbsp;RfC&nbsp;would close (formally or informally) or go stale, by analyzing more than 60 features of the text, Wikipedia page, and editor account information. The model achieved a 75 percent accuracy for predicting failure or success within one week after discussion started. Some more informative features for prediction, they found, include the length of the discussion, number of participants and replies, number of revisions to the article, popularity of and interest in the topic, experience of the discussion participants, and the level of vulgarity, negativity, and general aggression in the comments.</p>
<p>The model could one day be used by RfC initiators to monitor a discussion as it’s unfolding. “We think it could be useful for editors to know how to target their interventions,” Zhang says. “They could post [the RfC] to more [Wikipedia forums] or invite more people, if it looks like it’s in danger of not being resolved.”</p>
<p>The researchers suggest Wikipedia could develop tools to help closers organize lengthy discussions, flag persuasive arguments and opinion changes within a thread, and encourage collaborative closing of RfCs.</p>
<p>In the future, the model and proposed tools could potentially be used for other community platforms that involve large-scale discussions and deliberations. Zhang points to online city-and community-planning forums, where citizens weigh in on proposals. “People are discussing [the proposals] and voting on them, so the tools can help communities better understand the discussions … and would [also] be useful for the implementers of the proposals.”</p>
<p>Zhang, Im, and other researchers have now built an external <a href="http://wikum.org/" target="_blank">website for editors</a> of all levels of expertise to come together to learn from one another, and more easily monitor and close discussions. “The work of closer is pretty tough,” Zhang says, “so there’s a shortage of people looking to close these discussions, especially difficult, longer, and more consequential ones. This could help reduce the barrier to entry [for editors to become closers] and help them collaborate to close RfCs.”</p>
<p>“While it is surprising that a third of these discussions were never resolved, [what’s more] important are the reasons why discussions fail to come to closure, and the most interesting conclusions here come from the qualitative analyses,” says Robert Kraut, a professor emeritus of human-computer interactions at Carnegie Melon University. “Some [of the study’s] findings transcend Wikipedia and can apply to many discussion in other settings.” More work, he adds, could be done to improve the accuracy of the machine-learning model in order to provide more actionable insights to Wikipedia.</p>
<p>The study sheds light on how some RfC processes “deviate from established norms, leading to inefficiencies and biases,” says Dario Taraborelli, director of research at the Wikimedia Foundation. “The results indicate that the experience of participants and the length of a discussion are strongly predictive of the timely closure of an RfC. This brings new empirical evidence to the question of how to make governance-related discussions more accessible to newcomers and members of underrepresented groups.”</p>
Often, multiple Wikipedia editors will disagree on certain changes to articles or policies. Image: MIT NewsResearch, Machine learning, Data, Internet, Crowdsourcing, Social media, Technology and society, Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of EngineeringTo build new college, MIT seeks campus and alumni inputhttps://news.mit.edu/2018/schwarzman-college-computing-campus-alumni-input-1102
Opportunities and new challenges were at the forefront of discussions about the MIT Stephen A. Schwarzman College of Computing.Fri, 02 Nov 2018 12:59:59 -0400Peter Dizikes | MIT News Officehttps://news.mit.edu/2018/schwarzman-college-computing-campus-alumni-input-1102<p>Since <a href="http://news.mit.edu/2018/mit-reshapes-itself-stephen-schwarzman-college-of-computing-1015">announcing</a> the MIT Stephen A. Schwarzman College of Computing, Institute leaders have reached out to the campus and alumni communities in a series of forums, seeking ideas about the transformative new entity that will radically integrate computing with disciplines throughout MIT.</p>
<p>MIT Provost Martin A. Schmidt and Dean of the School of Engineering Anantha P. Chandrakasan engaged with students, faculty, and staff at forums on campus, where they presented outlines of the project and received dozens of public comments and questions. Additionally, Chandrakasan and Executive Vice President and Treasurer Israel Ruiz engaged with alumni in two webcast sessions that featured Q&amp;A about the college.</p>
<p>“Creating this new college requires us to think deeply and carefully about its structure,” Schmidt said at a forum for faculty on Oct. 18. That process should be firmly connected to the ideas and experiences of the MIT community as a whole, he said further at a student forum on Oct. 25, adding that the goal was to “engage you in the process of building the college together.”</p>
<p><strong>Community perspectives</strong></p>
<p>The discussions at the forums each had a slightly different flavor, generally reflecting the perspectives of the participants. The faculty forum, for instance, included professors from several fields concerned about maintaining a solid balance of disciplinary research at MIT.</p>
<p>The responsibilities of professors at the new college have yet to be fully defined. Many faculty will have joint appointments between the MIT Schwarzman College of Computing and existing MIT departments, an approach that both Schmidt and Chandrakasan acknowledged has had varying results in the past. As participants noted, some MIT faculty with joint appointments have thrived, but others have floundered, being pulled in different scholarly and administrative directions.&nbsp;</p>
<p>“We need to figure out how to make dual appointments work,” Chandrakasan said. Still, he noted that the “cross-cutting” structure of the college had enormous potential to integrate computing into the full range of disciplines at the Institute.</p>
<p>At a standing-room-only forum for MIT staff members on Oct. 25, with people lining the walls of Room 4-270, audience members offered comments and questions about the college’s proposed main building, MIT’s computing infrastructure, teaching, advising, the admissions process, and the need to hire motivated staff in the college’s most formative stages.</p>
<p>“It’s an opportunity to really do a whole-of-Institute solution to this challenge,” Schmidt said. “It’s going to test us.”</p>
<p>Multiple people at the student forum on Oct. 25 called for diversity among the college’s new faculty — a view Schmidt and Chandrakasan readily agreed with. The Institute leaders also emphasized the expansion of opportunities the college will provide for students, including more joint programs and degrees, and more student support.</p>
<p>“There will be more UROP opportunities, more resources, more faculty,” Chandrakasan said. Also, he noted, “We’re not going to change the undergraduate admissions process.” MIT Chancellor Cynthia Barnhart also spoke at the student forum.</p>
<p>At all three on-campus forums, audience participants commented upon the value of having Institute supporters share MIT’s goal of creating a “better world.” At the staff forum, one audience member advocated that MIT only accept funding from backers who were fully committed to democracy, and questioned the Institute’s connections with Saudi Arabia. Schmidt noted that MIT — as it has publicly announced — is currently reassessing MIT’s Institute-level engagements with entities of the Kingdom of Saudi Arabia.</p>
<p>At the student forum, audience members also raised queries about MIT’s mission and its relationships with donors; the issues cited included the precedent of naming the college after an individual, and the extent of MIT’s due diligence process during the creation of the college. Schmidt said the Institute had performed its due diligence well and developed the idea of the named college after extensive discussions; he also noted that faculty and students of the college would be able to develop a full range of intellectual and academic projects freely.</p>
<p>Audience members also stressed the generalized need to think critically about the impact of technology on society at a moment of social, political, and ecological uncertainty — and expressed a preference for the college to integrate ethics into its curriculum.</p>
<p>“This presents a real opportunity to get at that,” Chandrakasan responded.</p>
<p>On Oct. 30, the Alumni Association hosted two webcasts that featured Q&amp;A with Chandrakasan and Ruiz. Over 1,000 alumni from around the world registered for the virtual conversations, which were moderated by Vice President for Communications Nate Nickerson. Questions centered on how the cross-disciplinary aspirations of the college would find life, and on how ethics will be made to infuse the college and shape its graduates. In both sessions, alumni asked how they can participate in the pursuit of the college’s mission. “The alumni will be critical to our efforts,” said Ruiz. “They offer us great wisdom as we form the college, and they will serve as important points of connection for our faculty and students as they seek to understand all the ways that computing is shaping our world.”</p>
<p><strong>Helping every department</strong></p>
<p>The college is being developed thanks to a $350 million foundational gift from Mr. Schwarzman, the chairman, CEO, and co-founder of Blackstone, a leading global asset manager. It will serve as an interdisciplinary hub for research and teaching across all aspects of computing, while strengthening links between computing and other scholarly pursuits.</p>
<p>“The college has two goals,” said Chandrakasan at the forum for MIT staff members. “One is to advance computing, and one is to link computing to other other [fields]. … This allows us to optimize, unbundle, and rebundle, to make computing much more integrated across all disciplines.”</p>
<p>It also presents new organizational challenges. For decades, MIT has been largely organized around its five schools, which focus on engineering; science; architecture and planning; humanities, arts, and social sciences; and management. But as Chandrakasan emphasized in all three campus forums, the MIT Schwarzman College of Computing is intended to develop connections with all of those schools as well as other stand-alone institutes and programs on campus.</p>
<p>“This is about helping advance every department,” said Chandrakasan, who frequently referred to the importance of the college’s “bridge” function, meaning it can span the width of MIT to link students, faculty, and resources together.</p>
<p>For his part, Schmidt emphasized at the events that the college will accelerate the current trend in disciplinary transformation. He noted that the fields of economics and urban studies at MIT have both recently created joint degrees with computer science as a natural response to the ways data and computing power have enabled new modes of academic research.</p>
<p>The foundational gift is part of a $1 billion commitment MIT has made to the new college, which will be centered in a new campus building, include 50 new faculty and allow the Institute to create a new series of collaborative, interdisciplinary enterprises in research and education. The college is meant to address all aspects of computing, including the policy and ethical issues surrounding new technologies.</p>
<p>“Across the Institute there is great enthusiasm for this,” Chandrakasan added.</p>
<p><strong>“A unique opportunity to evolve”</strong></p>
<p>The MIT Schwarzman College of Computing is intended to open in the fall of 2019 and will be housed partly — but not entirely — in its new building. The timeline, Chandrakasan acknowledged, is “super-aggressive.”</p>
<p>Schmidt and Chandrakasan noted that many important issues were yet to be resolved. As part of the process of developing the college, the Institute is creating a task force and working groups to assess some of the critical issues MIT faces.</p>
<p>Some audience members at the forums also questioned why MIT would announce the creation of its new college at a time when some of the entity’s institutional features are unresolved. In response, Schmidt noted that the Institute benefits by being on the leading edge of computing, and that the creation of the college will only enhance that position. Community engagement, he noted, would help the Institute finalize its vision for the college.</p>
<p>“We’re not going to be able to answer all of [your] questions,” Schmidt said at the staff forum. To gain traction on unresolved matters, he added, “We think the task force model is an appropriate one.”</p>
<p>MIT intends to hire a dean for the college and begin the search process for new faculty during the current academic year. There are a few campus sites being considered as the location for the college’s main building, but not all elements of the college will be located in that building.</p>
<p>Overall, Schmidt concluded, the creation of the college has presented MIT with a unique opportunity to evolve in response to the prevalence of computing and its influence in so many spheres of life.</p>
<p>“Every campus in the country today has been grappling with the need,” Schmidt said. “We feel that MIT has come forward with a really compelling solution.”</p>
MIT Provost Martin A. Schmidt at an MIT forum about the new MIT Schwarzman College of Computing.Image: Jared CharneyArtificial intelligence, Machine learning, Faculty, Students, Staff, Community, Algorithms, Research, Computer science and technology, Technology and society, Ethics, Classes and programs, Administration, Special events and guest speakers, MIT Schwarzman College of ComputingFleets of drones could aid searches for lost hikershttps://news.mit.edu/2018/fleets-drones-help-searches-lost-hikers-1102
System allows drones to cooperatively explore terrain under thick forest canopies where GPS signals are unreliable.Thu, 01 Nov 2018 23:59:59 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/fleets-drones-help-searches-lost-hikers-1102<p>Finding lost hikers in forests can be a difficult and lengthy process, as helicopters and drones can’t get a glimpse through the thick tree canopy. Recently, it’s been proposed that autonomous drones, which can bob and weave through trees, could aid these searches. But the GPS signals used to guide the aircraft can be unreliable or nonexistent in forest environments.</p>
<p>In a paper being presented at the International Symposium on Experimental Robotics conference next week, MIT researchers describe an autonomous system for a fleet of drones to collaboratively search under dense forest canopies. The drones use only onboard computation and wireless communication — no GPS required.</p>
<p>Each autonomous quadrotor drone is equipped with laser-range finders for position estimation, localization, and path planning. As the drone flies around, it creates an individual 3-D map of the terrain. Algorithms help it recognize unexplored and already-searched spots, so it knows when it’s fully mapped an area. An off-board ground station fuses individual maps from multiple drones into a global 3-D map that can be monitored by human rescuers.</p>
<p>In a real-world implementation, though not in the current system, the drones would come equipped with object detection to identify a missing hiker. When located, the drone would tag the hiker’s location on the global map. Humans could then use this information to plan a rescue mission.</p>
<p>“Essentially, we’re replacing humans with a fleet of drones to make the search part of the search-and-rescue process more efficient,” says first author Yulun Tian, a graduate student in the Department of Aeronautics and Astronautics (AeroAstro).</p>
<p>The researchers tested multiple drones in simulations of randomly generated forests, and tested two drones in a forested area within NASA’s Langley Research Center. In both experiments, each drone mapped a roughly 20-square-meter area in about two to five minutes and collaboratively fused their maps together in real-time. The drones also performed well across several metrics, including overall speed and time to complete the mission, detection of forest features, and accurate merging of maps.</p>
<p>Co-authors on the paper are: Katherine Liu, a PhD student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and AeroAstro; Kyel Ok, a PhD student in CSAIL and the Department of Electrical Engineering and Computer Science; Loc Tran and Danette Allen of the NASA Langley Research Center; Nicholas Roy, an AeroAstro professor and CSAIL researcher; and Jonathan P. How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics.</p>
<div class="cms-placeholder-content-video"></div>
<p><strong>Exploring and mapping</strong></p>
<p>On each drone, the researchers mounted a LIDAR system, which creates a 2-D scan of the surrounding obstacles by shooting laser beams and measuring the reflected pulses. This can be used to detect trees; however, to drones, individual trees appear remarkably similar. If a drone can’t recognize a given tree, it can’t determine if it’s already explored an area.</p>
<p>The researchers programmed their drones to instead identify multiple trees’ orientations, which is far more distinctive. With this method, when the LIDAR signal returns a cluster of trees, an algorithm calculates the angles and distances between trees to identify that cluster. “Drones can use that as a unique signature to tell if they’ve visited this area before or if it’s a new area,” Tian says.</p>
<p>This feature-detection technique helps the ground station accurately merge maps. The drones generally explore an area in loops, producing scans as they go. The ground station continuously monitors the scans. When two drones loop around to the same cluster of trees, the ground station merges the maps by calculating the relative transformation between the drones, and then fusing the individual maps to maintain consistent orientations.</p>
<p>“Calculating that relative transformation tells you how you should align the two maps so it corresponds to exactly how the forest looks,” Tian says.</p>
<p>In the ground station, robotic navigation software called “simultaneous localization and mapping” (SLAM) — which both maps an unknown area and keeps track of an agent inside the area — uses the LIDAR input to localize and capture the position of the drones. This helps it fuse the maps accurately.</p>
<p>The end result is a map with 3-D terrain features. Trees appear as blocks of colored shades of blue to green, depending on height. Unexplored areas are dark but turn gray as they’re mapped by a drone. On-board path-planning software tells a drone to always explore these dark unexplored areas as it flies around. Producing a 3-D map is more reliable than simply attaching a camera to a drone and monitoring the video feed, Tian says. Transmitting video to a central station, for instance, requires a lot of bandwidth that may not be available in forested areas.</p>
<p><strong>More efficient searching</strong></p>
<p>A key innovation is a novel search strategy that let the drones more efficiently explore an area. According to a more traditional approach, a drone would always search the closest possible unknown area. However, that could be in any number of directions from the drone’s current position. The drone usually flies a short distance, and then stops to select a new direction.</p>
<p>“That doesn’t respect dynamics of drone [movement],” Tian says. “It has to stop and turn, so that means it’s very inefficient in terms of time and energy, and you can’t really pick up speed.”</p>
<p>&nbsp;Instead, the researchers’ drones explore the closest possible area, while considering their current direction. They believe this can help the drones maintain a more consistent velocity. This strategy — where the drone tends to travel in a spiral pattern — covers a search area much faster. “In search and rescue missions, time is very important,” Tian says.</p>
<p>In the paper, the researchers compared their new search strategy with a traditional method. Compared to that baseline, the researchers’ strategy helped the drones cover significantly more area, several minutes faster and with higher average speeds.</p>
<p>One limitation for practical use is that the drones still must communicate with an off-board ground station for map merging. In their outdoor experiment, the researchers had to set up a wireless router that connected each drone and the ground station. In the future, they hope to design the drones to communicate wirelessly when approaching one another, fuse their maps, and then cut communication when they separate. The ground station, in that case, would only be used to monitor the updated global map.</p>
MIT researchers describe an autonomous system for a fleet of drones to collaboratively search under dense forest canopies using only onboard computation and wireless communication — no GPS required.Images: Melanie GonickResearch, Algorithms, Computer science and technology, Autonomous vehicles, Drones, Aeronautical and astronautical engineering, Robotics, Robots, Computer vision, Technology and society, NASA, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of EngineeringYoussef Marzouk and Nicolas Hadjiconstantinou to direct the Center for Computational Engineeringhttps://news.mit.edu/2018/youssef-marzouk-nicolas-hadjiconstantinou-named-center-computational-engineering-co-directors-1031
New leadership team named for the Institute&#039;s interdisciplinary hub for advanced thinking in the science and engineering of computation.Wed, 31 Oct 2018 12:30:00 -0400School of Engineeringhttps://news.mit.edu/2018/youssef-marzouk-nicolas-hadjiconstantinou-named-center-computational-engineering-co-directors-1031<p>Youssef Marzouk and Nicolas Hadjiconstantinou have been named co-directors of MIT’s Center for Computational Engineering (CCE), effective immediately, Anantha Chandrakasan, dean of the School of Engineering, has announced.</p>
<p>“This is an exciting time for computation at MIT, and I’m delighted they have agreed to serve in this important role,” Chadarkasan says. “The CCE has become a hub for some of the most advanced thinking on the science and engineering of computation. Professor&nbsp;Marzouk and Professor&nbsp;Hadjiconstantinou’s deep connections to this community and its pioneering educational programs will make them important partners in our plans for the future.”&nbsp;&nbsp;</p>
<p>An associate professor in the Department of Aeronautics and Astronautics, Marzouk is also the director of MIT’s Aerospace Computational Design Laboratory and has served as co-director of graduate educational programs for the CCE. He is also a core member of the Statistics and Data Science Center in MIT's Institute for Data, Systems, and Society. His research focuses on uncertainty quantification, inverse problems, statistical inference, and large-scale Bayesian computation for complex physical systems, and on using these approaches to address modeling challenges in energy conversion and environmental applications.</p>
<p>Marzouk received his BS, MS, and PhD degrees in mechanical engineering at the Institute, and spent several years at Sandia National Laboratories before joining the faculty in 2009. He is a recipient of the Hertz Foundation doctoral thesis prize, the Sandia Laboratories Truman Fellowship, the U.S. Department of Energy Early Career Research Award, and the Junior Bose Award for Teaching Excellence from the MIT School of Engineering.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>
<p>Hadjiconstantinou is a professor in the Department of Mechanical Engineering and co-director for the CCE’s Computation for Design and Optimization program, as well as its computational science and engineering PhD program. His research interests include kinetic transport for small-scale fluid flow and solid-state heat transfer applications, molecular and stochastic simulation of nanoscale transport phenomena, and molecular and multiscale simulation method development. His research group uses theoretical molecular mechanics approaches, as well as molecular simulation techniques, to develop better understanding, as well as reliable models of nanoscale transport.</p>
<p>Hadjiconstantinou received a BA and MA in engineering from the University of Cambridge, and MS's in both mechanical engineering and physics from MIT, where he also earned his PhD in mechanical engineering. He is a former Lawrence Livermore Fellow and was awarded the Gustus L. Larson Award from the American Society of Mechanical Engineers.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>
<p>The Center for Computational Engineering was launched in 2008 and serves as a focal point for research and education in computational science and engineering at MIT. The center has its roots in the Computation for Design and Optimization (CDO) master’s degree program, which first started in 2005. CDO was incorporated into CCE when it was established, and in 2013 the center established a PhD program in computational science and engineering.</p>
<p>The center now comprises faculty and research partners from across the Institute. Its work focuses on advancing computational methodologies for scientific discovery and technological innovation across a spectrum of societally important application areas.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>
<p>The CCE’s education programs are, by construction, interdisciplinary. Students in the center’s doctoral program, for example, satisfy departmental requirements with participating partner departments (currently Aeronautics and Astronautics, Civil and Environmental Engineering, Chemical Engineering, Mechanical Engineering, Nuclear Science and Engineering, and Mathematics), but with enhancements that reflect an emphasis on computational engineering. This with-departments&nbsp;curricular structure is already serving as a model for other interdisciplinary doctoral programs at MIT, such as the PhD program in statistics administered within IDSS. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>
<p>Marzouk and Hadjiconstantinou replace Anthony Patera, the Ford Foundation Professor of Engineering in the Department of Mechanical Engineering, and Karen Willcox, a former MIT professor of aeronautics and astronautics.</p>
Youssef Marzouk (left) and Nicolas Hadjiconstantinou assume new roles at the Center for Computational Engineering at “an exciting time for computation at MIT,” says School of Engineering Dean Anantha Chandrakasan.Photo: Lillie Paquette/School of EngineeringSchool of Engineering, Mechanical engineering, Aeronautics and Astronautics, Civil and environmental engineering, Chemical engineering, Nuclear science and engineering, Mathematics, Classes and programs, Computer science and technology, Education, teaching, academics, Faculty, Collaboration, School of ScienceMachines that learn language more like kids dohttps://news.mit.edu/2018/machines-learn-language-human-interaction-1031
Computer model could improve human-machine interaction, provide insight into how children learn language.Wed, 31 Oct 2018 11:52:58 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/machines-learn-language-human-interaction-1031<p>Children learn language by observing their environment, listening to the people around them, and connecting the dots between what they see and hear. Among other things, this helps children establish their language’s word order, such as where subjects and verbs fall in a sentence.</p>
<p>In computing, learning language is the task of syntactic and semantic parsers. These systems are trained on sentences annotated by humans that describe the structure and meaning behind words. Parsers are becoming increasingly important for web searches, natural-language database querying, and voice-recognition systems such as Alexa and Siri. Soon, they may also be used for home robotics.</p>
<p>But gathering the annotation data can be time-consuming and difficult for less common languages. Additionally, humans don’t always agree on the annotations, and the annotations themselves may not accurately reflect how people naturally speak.</p>
<p>In a paper being presented at this week’s Empirical Methods in Natural Language Processing conference, MIT researchers describe a parser that learns through observation to more closely mimic a child’s language-acquisition process,&nbsp;which could greatly extend the parser’s capabilities. To learn the structure of language, the parser observes captioned videos, with no other information, and associates the words with recorded objects and actions. Given a new sentence, the parser can then use what it’s learned about the structure of the language to accurately predict a sentence’s meaning, without the video.</p>
<p>This “weakly supervised” approach — meaning it requires limited training data — mimics how children can observe the world around them and learn language, without anyone providing direct context. The approach could expand the types of data and reduce the effort needed for training parsers, according to the researchers. A few directly annotated sentences, for instance, could be combined with many captioned videos, which are easier to come by, to improve performance.</p>
<p>In the future, the parser could be used to improve natural interaction between humans and personal robots. A robot equipped with the parser, for instance, could constantly observe its environment to reinforce its understanding of spoken commands, including when the spoken sentences aren’t fully grammatical or clear. “People talk to each other in partial sentences, run-on thoughts, and jumbled language. You want a robot in your home that will adapt to their particular way of speaking … and still figure out what they mean,” says co-author Andrei Barbu, a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds, and Machines (CBMM) within MIT’s McGovern Institute.</p>
<p>The parser could also help researchers better understand how young children learn language. “A child has access to redundant, complementary information from different modalities, including hearing parents and siblings talk about the world, as well as tactile information and visual information, [which help him or her] to understand the world,” says co-author Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. “It’s an amazing puzzle, to process all this simultaneous sensory input. This work is part of bigger piece to understand how this kind of learning happens in the world.”</p>
<p>Co-authors on the paper are: first author Candace Ross, a graduate student in the Department of Electrical Engineering and Computer Science and CSAIL, and a researcher in CBMM; Yevgeni Berzak PhD ’17, a postdoc in the Computational Psycholinguistics Group in the Department of Brain and Cognitive Sciences; and CSAIL graduate student Battushig Myanganbayar.</p>
<p><strong>Visual learner</strong></p>
<p>For their work, the researchers combined a semantic parser with a computer-vision component trained in object, human, and activity recognition in video. Semantic parsers are generally trained on sentences annotated with code that ascribes meaning to each word and the relationships between the words. Some have been trained on still images or computer simulations.</p>
<p>The new parser is the first to be trained using video, Ross says. In part, videos are more useful in reducing ambiguity. If the parser is unsure about, say, an action or object in a sentence, it can reference the video to clear things up. “There are temporal components —&nbsp;objects interacting with each other and with people —&nbsp;and high-level properties you wouldn’t see in a still image or just in language,” Ross says.</p>
<p>The researchers compiled a dataset of about 400 videos depicting people carrying out a number of actions, including picking up an object or putting it down, and walking toward an object. Participants on the crowdsourcing platform Mechanical Turk then provided 1,200 captions for those videos. They set aside 840 video-caption examples for training and tuning, and used 360 for testing. One advantage of using vision-based parsing is “you don’t need nearly as much data — although if you had [the data], you could scale up to huge datasets,” Barbu says.</p>
<p>In training, the researchers gave the parser the objective of determining whether a sentence accurately describes a given video. They fed the parser a video and matching caption. The parser extracts possible meanings of the caption as logical mathematical expressions. The sentence, “The woman is picking up an apple,” for instance, may be expressed as: <em>λxy.</em> woman <em>x,</em> pick_up <em>x y</em>, apple <em>y</em>.</p>
<p>Those expressions and the video are inputted to the computer-vision algorithm, called “Sentence Tracker,” developed by Barbu and other researchers. The algorithm looks at each video frame to track how objects and people transform over time, to determine if actions are playing out as described. In this way, it determines if the meaning is possibly true of the video.</p>
<p><strong>Connecting the dots</strong></p>
<p>The expression with the most closely matching representations for objects, humans, and actions becomes the most likely meaning of the caption. The expression, initially, may refer to many different objects and actions in the video, but the set of possible meanings serves as a training signal that helps the parser continuously winnow down possibilities. “By assuming that all of the sentences must follow the same rules, that they all come from the same language, and seeing many captioned videos, you can narrow down the meanings further,” Barbu says.</p>
<p>In short, the parser learns through passive observation: To determine if a caption is true of a video, the parser by necessity must identify the highest probability meaning of the caption. “The only way to figure out if the sentence is true of a video [is] to go through this intermediate step of, ‘What does the sentence mean?’ Otherwise, you have no idea how to connect the two,” Barbu explains. “We don’t give the system the meaning for the sentence. We say, ‘There’s a sentence and a video. The sentence has to be true of the video. Figure out some intermediate representation that makes it true of the video.’”</p>
<p>The training produces a syntactic and semantic grammar for the words it’s learned. Given a new sentence, the parser no longer requires videos, but leverages its grammar and lexicon to determine sentence structure and meaning.</p>
<p>Ultimately, this process is learning “as if you’re a kid,” Barbu says. “You see world around you and hear people speaking to learn meaning. One day, I can give you a sentence and ask what it means and, even without a visual, you know the meaning.”</p>
<p>“This research is exactly the right direction for natural language processing,” says Stefanie Tellex,&nbsp;a professor of computer science at Brown University who focuses on helping robots use natural language to communicate with humans. “To interpret&nbsp;grounded language, we need semantic representations, but it is not&nbsp;practicable to make it available at training time. Instead, this work captures representations of compositional&nbsp;structure using context from captioned videos. This is the paper I have been waiting for!”</p>
<p>In future work, the researchers are interested in modeling interactions, not just passive observations. “Children interact with the environment as they’re learning. Our idea is to have a model that would also use perception to learn,” Ross says.</p>
<p>This work was supported, in part, by the CBMM, the National Science Foundation, a Ford Foundation Graduate Research Fellowship, the Toyota Research Institute, and the MIT-IBM Brain-Inspired Multimedia Comprehension project.</p>
MIT researchers have developed a “semantic parser” that learns through observation to more closely mimic a child’s language-acquisition process, which could greatly extend computing’s capabilities.Photo: MIT NewsResearch, Language, Machine learning, Artificial intelligence, Data, Computer vision, Human-computer interaction, McGovern Institute, Center for Brains Minds and Machines, Robots, Robotics, National Science Foundation (NSF), Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of EngineeringModel paves way for faster, more efficient translations of more languageshttps://news.mit.edu/2018/unsupervised-model-faster-computer-translations-languages-1030
New system may open up the world’s roughly 7,000 spoken languages to computer-based translation.Tue, 30 Oct 2018 10:59:55 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/unsupervised-model-faster-computer-translations-languages-1030<p>MIT researchers have developed a novel “unsupervised” language translation model —&nbsp;meaning it runs without the need for human annotations and guidance — that could lead to faster, more efficient computer-based translations of far more languages.</p>
<p>Translation systems from Google, Facebook, and Amazon require training models to look for patterns in millions of documents —&nbsp;such as legal and political documents, or news articles —&nbsp;that have been translated into various languages by humans. Given new words in one language, they can then find the matching words and phrases in the other language.</p>
<p>But this translational data is time consuming and difficult to gather, and simply may not exist for many of the 7,000 languages spoken worldwide. Recently, researchers have been developing “monolingual” models that make translations between texts in two languages, but without direct translational information between the two.</p>
<p>In a paper being presented this week at the Conference on Empirical Methods in Natural Language Processing, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) describe a model that runs faster and more efficiently than these monolingual models.</p>
<p>The model leverages a metric in statistics, called Gromov-Wasserstein distance, that essentially measures distances between points in one computational space and matches them to similarly distanced points in another space. They apply that technique to “word embeddings” of two languages, which are words represented as vectors — basically, arrays of numbers —&nbsp;with words of similar meanings clustered closer together. In doing so, the model quickly aligns the words, or vectors, in both embeddings that are most closely correlated by relative distances, meaning they’re likely to be direct translations.</p>
<p>In experiments, the researchers’ model performed as accurately as state-of-the-art monolingual models —&nbsp;and sometimes more accurately —&nbsp;but much more quickly and using only a fraction of the computation power.</p>
<p>“The model sees the words in the two languages as sets of vectors, and maps [those vectors] from one set to the other by essentially preserving relationships,” says the paper’s co-author Tommi Jaakkola, a CSAIL researcher and the Thomas Siebel Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. “The approach could help translate low-resource languages or dialects, so long as they come with enough monolingual content.”</p>
<p>The model represents a step toward one of the major goals of machine translation, which is fully unsupervised word alignment, says first author David Alvarez-Melis, a CSAIL PhD student: “If you don’t have any data that matches two languages … you can map two languages and, using these distance measurements, align them.”</p>
<p><strong>Relationships matter most</strong></p>
<p>Aligning word embeddings for unsupervised machine translation isn’t a new concept. Recent work trains neural networks to match vectors directly in word embeddings, or matrices, from two languages together. But these methods require a lot of tweaking during training to get the alignments exactly right, which is inefficient and time consuming.</p>
<p>Measuring and matching vectors based on relational distances, on the other hand, is a far more efficient method that doesn’t require much fine-tuning. No matter where word vectors fall in a given matrix, the relationship between the words, meaning their distances, will remain the same. For instance, the vector for “father” may fall in completely different areas in two matrices. But vectors for “father” and “mother” will most likely always be close together.</p>
<p>“Those distances are invariant,” Alvarez-Melis says. “By looking at distance, and not the absolute positions of vectors, then you can skip the alignment and go directly to matching the correspondences between vectors.”</p>
<p>That’s where Gromov-Wasserstein comes in handy. The technique has been used in computer science for, say, helping align image pixels in graphic design. But the metric seemed “tailor made” for word alignment, Alvarez-Melis says: “If there are points, or words, that are close together in one space, Gromov-Wasserstein is automatically going to try to find the corresponding cluster of points in the other space.”</p>
<p>For training and testing, the researchers used a dataset of publicly available word embeddings, called FASTTEXT, with 110 language pairs. In these embeddings, and others, words that appear more and more frequently in similar contexts have closely matching vectors. “Mother” and “father” will usually be close together but both farther away from, say, “house.”</p>
<p><strong>Providing a “soft translation”</strong></p>
<p>The model notes vectors that are closely related yet different from the others, and assigns a probability that similarly distanced vectors in the other embedding will correspond. It’s kind of like a “soft translation,” Alvarez-Melis says, “because instead of just returning a single word translation, it tells you ‘this vector, or word, has a strong correspondence with this word, or words, in the other language.’”</p>
<p>An example would be in the months of the year, which appear closely together in many languages. The model will see a cluster of 12 vectors that are clustered in one embedding and a remarkably similar cluster in the other embedding. “The model doesn’t know these are months,” Alvarez-Melis says. “It just knows there is a cluster of 12 points that aligns with a cluster of 12 points in the other language, but they’re different to the rest of the words, so they probably go together well. By finding these correspondences for each word, it then aligns the whole space simultaneously.”</p>
<p>The researchers hope the work serves as a “feasibility check,” Jaakkola says, to apply Gromov-Wasserstein method to machine-translation systems to run faster, more efficiently, and gain access to many more languages.</p>
<p>Additionally, a possible perk of the model is that it automatically produces a value that can be interpreted as quantifying, on a numerical scale, the similarity between languages. This may be useful for linguistics studies, the researchers say. The model calculates how distant all vectors are from one another in two embeddings, which depends on sentence structure and other factors. If vectors are all really close, they’ll score closer to 0, and the farther apart they are, the higher the score. Similar Romance languages such as French and Italian, for instance, score close to 1, while classic Chinese scores between 6 and 9 with other major languages.</p>
<p>“This gives you a nice, simple number for how similar languages are … and can be used to draw insights about the relationships between languages,” Alvarez-Melis says.</p>
The new model measures distances between words with similar meanings in “word embeddings,” and then aligns the words in both embeddings that are most closely correlated by relative distances, meaning they’re most likely to be direct translations of one another.Courtesy of the researchersResearch, Language, Machine learning, Artificial intelligence, Data, Algorithms, Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), IDSS, Electrical Engineering & Computer Science (eecs), School of EngineeringDefending against Spectre and Meltdown attackshttps://news.mit.edu/2018/mit-csail-dawg-better-security-against-spectre-meltdown-attacks-1018
New system breaks up cache memory more efficiently to better protect computer systems against timing attacks.Thu, 18 Oct 2018 15:30:00 -0400Adam Conner-Simons | CSAILhttps://news.mit.edu/2018/mit-csail-dawg-better-security-against-spectre-meltdown-attacks-1018<p>In January the technology world was rattled by the discovery of Meltdown and Spectre, two major security vulnerabilities in the processors that can be found in virtually every computer on the planet.</p>
<p>Perhaps the most alarming thing about these vulnerabilities is that they didn’t stem from normal software bugs or physical CPU problems. Instead, they arose from the architecture of the processors themselves — that is, the millions of transistors that work together to execute operations.</p>
<p>“These attacks fundamentally changed our understanding of what’s trustworthy in a system, and force us to re-examine where we devote security resources,” says Ilia Lebedev, a PhD student at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “They’ve shown that we need to be paying much more attention to the microarchitecture of systems.”</p>
<p>Lebedev and his colleagues believe that they’ve made an important new breakthrough in this field, with an approach that makes it much harder for hackers to cash in on such vulnerabilities. Their method could have immediate applications in cloud computing, especially for fields like medicine and finance that currently limit their cloud-based features because of security concerns.</p>
<p>With Meltdown and Spectre, hackers exploited the fact that operations all take slightly different amounts of time to execute. To use a simplified example, someone who’s guessing a PIN might first try combinations “1111” through “9111." If the first eight guesses take the same amount of time, and "9111" takes a nanosecond longer, then that one most likely has at least the "9" right, and the attacker can then start guessing "9111" through "9911", and so on and so forth.</p>
<p>An operation that’s especially vulnerable to these so-called “timing attacks” is accessing memory. If systems always had to wait for memory before doing the next step of an action, they’d spend much of their time sitting idle.</p>
<p>To keep performance up, engineers employ a trick: They give the processor the power to execute multiple instructions while it waits for memory —&nbsp;and then, once memory is ready, discards the ones that weren’t needed. Hardware designers call this&nbsp;“speculative execution.”</p>
<p>While it pays off in performance speed, it also creates new security issues. Specifically, the attacker could make the processor speculatively execute some code to read a part of memory it shouldn’t be able to. Even if the code fails, it could still leak data that the attacker can then access.</p>
<p>A common way to try to prevent such attacks is to split up memory so that it’s not all stored in one area. Imagine an industrial kitchen shared by chefs who all want to keep their recipes secret. One approach would be to have the chefs set up their work on different sides —&nbsp;that’s essentially what happens with the Cache Allocation Technology&nbsp;(CAT) that Intel started using in 2016. But such a system is still quite insecure, since one chef can get a pretty good idea of others’ recipes by seeing which pots and pans they take from the common area.</p>
<p>In contrast, the MIT CSAIL team’s approach is the equivalent of building walls to split the kitchen into separate spaces, and ensuring that everyone only knows their own ingredients and appliances. (This approach is a form of so-called “secure way partitioning”; the chefs&nbsp;in the case of cache memory&nbsp;are referred to as “protection domains.”)<br />
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br />
As a playful counterpoint to Intel’s CAT system, the researchers dubbed their method “DAWG”, which stands for “Dynamically Allocated Way Guard.” (The dynamic&nbsp;part means that DAWG can split the cache into multiple buckets whose size can vary over time.)</p>
<p>Lebedev co-wrote a new <a href="http://people.csail.mit.edu/vlk/dawg-micro18.pdf" target="_blank">paper</a> about the project with lead author Vladimir Kiriansky and MIT professors Saman Amarasinghe, Srini Devadas, and Joel Emer. They will present their findings next week at the annual IEEE/ACM International Symposium on Microarchitecture (MICRO) in Fukuoka City, Japan.</p>
<p>“This paper dives into how to fully isolate one program's side-effects from percolating through to another program through the cache,” says Mohit Tiwari, an assistant professor at the University of Texas at Austin who was not involved in the project. “This work secures a channel that’s one of the most popular to use for attacks.”</p>
<p>In tests, the team also found that the system was comparable with CAT on performance. They say that DAWG requires very minimal modifications to modern operating systems.</p>
<p>“We think this is an important step forward in giving computer architects, cloud providers, and other IT professionals a better way to efficiently and dynamically allocate resources,” says Kiriansky, a PhD student at CSAIL. “It establishes clear boundaries for where sharing should and should not happen, so that programs with sensitive information can keep that data reasonably secure.”</p>
<p>The team is quick to caution that DAWG can’t yet defend against all speculative attacks. However, they have experimentally demonstrated that it is a foolproof solution to a broad range of non-speculative attacks against cryptographic software.</p>
<p>Lebedev says that the growing prevalence of these types of attacks demonstrates that, contrary to popular tech-CEO wisdom, more information sharing isn’t always a good thing.</p>
<p>“There’s a tension between performance and security that’s come to a head for a community of architecture designers that have always tried to share as much as possible in as many places as possible,” he says. “On the other hand, if security was the only priority, we’d have separate computers for every program we want to run so that no information could ever leak, which obviously isn’t practical. DAWG is part of a growing body of work trying to reconcile these two opposing forces.”</p>
<p>It’s worth recognizing that the sudden attention on timing attacks reflects the paradoxical fact that computer security has actually gotten a lot better in the last 20 years.</p>
<p>“A decade ago software wasn’t written as well as it is today, which means that other attacks were a lot easier to perform,” says Kiriansky. “As other aspects of security have become harder to carry out, these microarchitectural attacks have become more appealing, though they’re still fortunately just a small piece in an arsenal of actions that an attacker would have to take to actually do damage.”</p>
<p>The team is now working to improve DAWG so that it can stop all currently known speculative-execution attacks. In the meantime, they’re hopeful that companies such as Intel will be interested in adopting their idea —&nbsp;or others like it —&nbsp;to minimize the chance of future data breaches.</p>
<p>“These kinds of attacks have become a lot easier thanks to these vulnerabilities,” says Kiriansky. “With all the negative PR that’s come up, companies like Intel have the incentives to get this right. The stars are aligned to make an approach like this happen.”</p>
A new system developed at CSAIL was shown to have stronger security guarantees than Intel's existing approach for preventing so-called "timing attacks" like Meltdown and Spectre, made possible by hardware vulnerabilities. Image courtesy of Graz University of TechnologySchool of Engineering, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), Computer science and technology, Cyber security, Security studies and military, internet of things, Technology and societyCryptographic protocol enables greater collaboration in drug discoveryhttps://news.mit.edu/2018/cryptographic-protocol-collaboration-drug-discovery-1018
Neural network that securely finds potential drugs could encourage large-scale pooling of sensitive data.Thu, 18 Oct 2018 14:00:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/cryptographic-protocol-collaboration-drug-discovery-1018<p>MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery.</p>
<p>Datasets of drug-target interactions (DTI), which show whether candidate compounds act on target proteins, are critical in helping researchers develop new medications. Models can be trained to crunch datasets of known DTIs and then, using that information, find novel drug candidates.</p>
<p>In recent years, pharmaceutical firms, universities, and other entities have become open to pooling pharmacological data into larger databases that can greatly improve training of these models. Due to intellectual property matters and other privacy concerns, however, these datasets remain limited in scope. Cryptography methods to secure the data are so computationally intensive they don’t scale well to datasets beyond, say, tens of thousands of DTIs, which is relatively small.</p>
<p>In a paper published today in <em>Science</em>, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) describe a neural network securely trained and tested on a dataset of more than a million DTIs. The network leverages modern cryptographic tools and optimization techniques to keep the input data private, while running quickly and efficiently at scale.</p>
<p>The team’s experiments show the network performs faster and more accurately than existing approaches; it can process massive datasets in days, whereas other cryptographic frameworks would take months. Moreover, the network identified several novel interactions, including one between the leukemia drug imatinib and an enzyme ErbB4 — mutations of which have been associated with cancer — which could have clinical significance.</p>
<p>“People realize they need to pool their data to greatly accelerate the drug discovery process and enable us, together, to make scientific advances in solving important human diseases, such as cancer or diabetes. But they don’t have good ways of doing it,” says corresponding author Bonnie Berger, the Simons Professor of Mathematics and a principal investigator at CSAIL. “With this work, we provide a way for these entities to efficiently pool and analyze their data at a very large scale.”</p>
<p>Joining Berger on the paper are co-first authors Brian Hie and Hyunghoon Cho, both graduate students in electrical engineering and computer science and researchers in CSAIL’s Computation and Biology group.</p>
<p><strong>“Secret sharing” data</strong></p>
<p>The new paper builds on previous <a href="http://news.mit.edu/2018/protecting-confidentiality-genomic-studies-0507">work</a> by the researchers in protecting patient confidentiality in genomic studies, which find links between particular genetic variants and incidence of disease. That genomic data could potentially reveal personal information, so patients can be reluctant to enroll in the studies. In that work, Berger, Cho, and a former Stanford University PhD student developed a protocol based on a cryptography framework called “secret sharing,” which securely and efficiently analyzes datasets of a million genomes. In contrast, existing proposals could handle only a few thousand genomes.</p>
<p>Secret sharing is used in multiparty computation, where sensitive data is divided into separate “shares” among multiple servers. Throughout computation, each party will always have only its share of the data, which appears fully random. Collectively, however, the servers can still communicate and perform useful operations on the underlying private data. At the end of the computation, when a result is needed, the parties combine their shares to reveal the result.</p>
<p>“We used our previous work as a basis to apply secret sharing to the problem of pharmacological collaboration, but it didn’t work right off the shelf,” Berger says.</p>
<p>A key innovation was reducing the computation needed in training and testing. Existing predictive drug-discovery models represent the chemical and protein structures of DTIs as graphs or matrices. These approaches, however, scale quadratically, or squared, with the number of DTIs in the dataset. Basically, processing these representations becomes extremely computationally intensive as the size of the dataset grows. “While that may be fine for working with the raw data, if you try that in secure computation, it’s infeasible,” Hie says.</p>
<p>The researchers instead trained a neural network that relies on linear calculations, which scale far more efficiently with the data. “We absolutely needed scalability, because we’re trying to provide a way to pool data together [into] much larger datasets,” Cho says.</p>
<p>The researchers trained a neural network on the STITCH dataset, which has 1.5 million DTIs, making it the largest publicly available dataset of its kind. In training, the network encodes each drug compound and protein structure as a simple vector representation. This essentially condenses the complicated structures as 1s and 0s that a computer can easily process. From those vectors, the network then learns the patterns of interactions and noninteractions. Fed new pairs of compounds and protein structures, the network then predicts if they’ll interact.</p>
<p>The network also has an architecture optimized for efficiency and security. Each layer of a neural network requires some activation function that determines how to send the information to the next layer. In their network, the researchers used an efficient activation function called a rectified linear unit (ReLU). This function requires only a single, secure numerical comparison of an interaction to determine whether to send (1) or not send (0) the data to the next layer, while also never revealing anything about the actual data. This operation can be more efficient in secure computation compared to more complex functions, so it reduces computational burden while ensuring data privacy.</p>
<p>“The reason that’s important is we want to do this within the secret sharing framework … and we don’t want to ramp up the computational overhead,” Berger says. In the end, “no parameters of the model are revealed and all input data — the drugs, targets, and interactions —&nbsp;are kept private.”</p>
<p><strong>Finding interactions</strong></p>
<p>The researchers pitted their network against several state-of-the-art, plaintext (unencrypted) models on a portion of known DTIs from DrugBank, a popular dataset containing about 2,000 DTIs. In addition to keeping the data private, the researchers’ network outperformed all of the models in prediction accuracy. Only two baseline models could reasonably scale to the STITCH dataset, and the researchers’ model achieved nearly double the accuracy of those models.</p>
<p>The researchers also tested drug-target pairs with no listed interactions in STITCH, and found several clinically established drug interactions that weren’t listed in the database but should be. In the paper, the researchers list the top strongest predictions, including: droloxifene and an estrogen receptor, which reached phase III clinical trials as a treatment for breast cancer; and seocalcitol and a vitamin D receptor to treat other cancers. Cho and Hie independently validated the highest-scoring novel interactions via contract research organizations.</p>
<p>The work could be "revolutionizing” for predictive drug discovery, says Artemis Hatzigeorgiou, a professor of bioinformatics at the University of Thessaly in Greece. “Having entered the era of big data in pharmacogenetics, it is possible for the first time to retrieve a dataset of this unprecedented big size from patient data. Similar to the learning procedure of a human brain, artificial neural networks need a critical mass of data in order to provide confident decisions,” Hatzigeorgiou says. “Now is possible the use of millions of data to train an artificial neural network toward the identification of unknown drug-target interactions. Under such conditions, it is not a surprise that this trained model outperforms all existing methods on drug discovery.”</p>
<p>Next, the researchers are working with partners to establish their collaborative pipeline in a real-world setting. “We are interested in putting together an environment for secure computation, so we can run our secure protocol with real data,” Cho says.</p>
MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets while keeping the data private, which could enable broader pooling of sensitive pharmacological data for predictive drug discovery.Image: Hie, Cho, BergerResearch, Cryptography, Privacy, Biology, Data, Health science and technology, Drug development, Machine learning, Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Mathematics, Electrical Engineering & Computer Science (eecs), School of Engineering, School of Science, MedicineA step toward personalized, automated smart homeshttps://news.mit.edu/2018/AI-identifies-people-indoor-smart-homes-1017
System that automatically identifies people moving around indoors could enable self-adjusting homes.Wed, 17 Oct 2018 00:00:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/AI-identifies-people-indoor-smart-homes-1017<p>Developing automated systems that track occupants and self-adapt to their preferences is a major next step for the future of smart homes. When you walk into a room, for instance, a system could set to your preferred temperature. Or when you sit on the couch, a system could instantly flick the television to your favorite channel.</p>
<p>But enabling a home system to recognize occupants as they move around the house is a more complex problem. Recently, <a href="http://news.mit.edu/2017/dina-katabi-csail-team-develop-wireless-system-to-detect-walking-speeds-0501">systems</a> have been built that localize humans by measuring the reflections of wireless signals off their bodies. But these systems can’t identify the individuals. Other systems can identify people, but only if they’re always carrying their mobile devices. Both systems also rely on tracking signals that could be weak or get blocked by various structures.</p>
<p>MIT researchers have built a system that takes a step toward fully automated smart home by identifying occupants, even when they’re not carrying mobile devices. The system, called Duet, uses reflected wireless signals to localize individuals. But it also incorporates algorithms that ping nearby mobile devices to predict the individuals’ identities, based on who last used the device and their predicted movement trajectory. It also uses logic to figure out who’s who, even in signal-denied areas.</p>
<p>“Smart homes are still based on explicit input from apps or telling Alexa to do something. Ideally, we want homes to be more reactive to what we do, to adapt to us,” says Deepak Vasisht, a PhD student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author on a paper describing the system that was presented at last week’s Ubicomp conference. “If you enable location awareness and identification awareness for smart homes, you could do this automatically. Your home knows it’s you walking, and where you’re walking, and it can update itself.”</p>
<p>Experiments done in a two-bedroom apartment with four people and an office with nine people, over two weeks, showed the system can identify individuals with 96 percent and 94 percent accuracy, respectively, including when people weren’t carrying their smartphones or were in blocked areas.</p>
<p>But the system isn’t just novelty. Duet could potentially be used to recognize intruders or ensure visitors don’t enter private areas of your home. Moreover, Vasisht says, the system could capture behavioral-analytics insights for health care applications. Someone suffering from depression, for instance, may move around more or less, depending on how they’re feeling on any given day. Such information, collected over time, could be valuable for monitoring and treatment.</p>
<p>“In behavioral studies, you care about how people are moving over time and how people are behaving,” Vasisht says. “All those questions can be answered by getting information on people’s locations and how they’re moving.”</p>
<p>The researchers envision that their system would be used with explicit consent from anyone who would be identified and tracked with Duet. If needed, they could also develop an app for users to grant or revoke Duet’s access to their location information at any time, Vasisht adds.</p>
<p>Co-authors on the paper are: Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science; former CSAIL researcher Anubhav Jain ’16; and CSAIL PhD students Chen-Yu Hsu and Zachary Kabelac.</p>
<p><strong>Tracking and identification</strong></p>
<p>Duet is a wireless sensor installed on a wall that’s about a foot and a half squared. It incorporates a floor map with annotated areas, such as the bedroom, kitchen, bed, and living room couch. It also collects identification tags from the occupants’ phones.</p>
<p>The system builds upon a device-based <a href="http://news.mit.edu/2016/wireless-tech-means-safer-drones-smarter-homes-password-free-wifi-0331">localization system</a> built by Vasisht, Katabi, and other researchers that tracks individuals within tens of centimeters, based on wireless signal reflections from their devices. It does so by using a central node to calculate the time it takes the signals to hit a person’s device and travel back. In experiments, the system was able to pinpoint where people were in a two-bedroom apartment and in a café.</p>
<p>The system, however, relied on people carrying mobile devices. “But in building [Duet] we realized, at home you don’t always carry your phone,” Vasisht says. “Most people leave devices on desks or tables, and walk around the house.”</p>
<p>The researchers combined their device-based localization with a device-free tracking system, called <a href="http://witrack.csail.mit.edu/">WiTrack</a>, developed by Katabi and other CSAIL researchers, that localizes people by measuring the reflections of wireless signals off their bodies.</p>
<p>Duet locates a smartphone and correlates its movement with individual movement captured by the device-free localization. If both are moving in tightly correlated trajectories, the system pairs the device with the individual and, therefore, knows the identity of the individual.</p>
<p>To ensure Duet knows someone’s identity when they’re away from their device, the researchers designed the system to capture the power profile of the signal received from the phone when it’s used. That profile changes, depending on the orientation of the signal, and that change be mapped to an individual’s trajectory to identify them. For example, when a phone is used and then put down, the system will capture the initial power profile. Then it will estimate how the power profile would look if it were still being carried along a path by a nearby moving individual. The closer the changing power profile correlates to the moving individual’s path, the more likely it is that individual owns the phone.</p>
<p><strong>Logical thinking</strong></p>
<p>One final issue is that structures such as bathroom tiles, television screens, mirrors, and various metal equipment can block signals.</p>
<p>To compensate for that, the researchers incorporated probabilistic algorithms to apply logical reasoning to localization. To do so, they designed the system to recognize entrance and exit boundaries of specific spaces in the home, such as doors to each room, the bedside, and the side of a couch. At any moment, the system will recognize the most likely identity for each individual in each boundary. It then infers who is who by process of elimination.</p>
<p>Suppose an apartment has two occupants: Alisha and Betsy. Duet sees Alisha and Betsy walk into the living room, by pairing their smartphone motion with their movement trajectories. Both then leave their phones on a nearby coffee table to charge —&nbsp;Betsy goes into the bedroom to nap; Alisha stays on the couch to watch television. Duet infers that Betsy has entered the bed boundary and didn’t exit, so must be on the bed. After a while, Alisha and Betsy move into, say, the kitchen —&nbsp;and the signal drops. Duet reasons that two people are in the kitchen, but it doesn’t know their identities. When Betsy returns to the living room and picks up her phone, however, the system automatically re-tags the individual as Betsy. By process of elimination, the other person still in the kitchen is Alisha.</p>
<p>“There are blind spots in homes where systems won’t work. But, because you have logical framework, you can make these inferences,” Vasisht says.</p>
<p>“Duet takes a smart approach of combining the location of different devices and associating it to humans, and leverages device-free localization techniques for localizing humans,” says Ranveer Chandra, a principal researcher at Microsoft, who was not involved in the work. “Accurately determining the location of all residents in a home has the potential to significantly enhance the in-home experience of users. … The home assistant can personalize the responses based on who all are around it; the temperature can be automatically controlled based on personal preferences, thereby resulting in energy savings. Future robots in the home could be more intelligent if they knew who was where in the house. The potential is endless.”</p>
<p>Next, the researchers aim for long-term deployments of Duet in more spaces and to provide high-level analytic services for applications such as health monitoring and responsive smart homes.</p>
MIT researchers have built a system that takes a step toward fully automated smart homes, by identifying occupants even when they’re not carrying mobile devices.Image: Chelsea Turner, MITResearch, Computer science and technology, Wireless, Mobile devices, Behavior, Mental health, Health sciences and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of EngineeringAutomated system identifies dense tissue, a risk factor for breast cancer, in mammogramshttps://news.mit.edu/2018/AI-identifies-dense-tissue-breast-cancer-mammograms-1016
Deep-learning model has been used successfully on patients, may lead to more consistent screening procedures. Tue, 16 Oct 2018 11:09:26 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/AI-identifies-dense-tissue-breast-cancer-mammograms-1016<p>Researchers from MIT and Massachusetts General Hospital have developed an automated model that assesses dense breast tissue in mammograms — which is an independent risk factor for breast cancer — as reliably as expert radiologists.</p>
<p>This marks the first time a deep-learning model of its kind has successfully been used in a clinic on real patients, according to the researchers. With broad implementation, the researchers hope the model can help bring greater reliability to breast density assessments across the nation.</p>
<p>It’s estimated that more than 40 percent of U.S. women have dense breast tissue, which alone increases the risk of breast cancer. Moreover, dense tissue can mask cancers on the mammogram, making screening more difficult. As a result, 30 U.S. states mandate that women must be notified if their mammograms indicate they have dense breasts.</p>
<p>But breast density assessments rely on subjective human assessment. Due to many factors, results vary — sometimes dramatically — across radiologists. The MIT and MGH researchers trained a deep-learning model on tens of thousands of high-quality digital mammograms to learn to distinguish different types of breast tissue, from fatty to extremely dense, based on expert assessments. Given a new mammogram, the model can then identify a density measurement that closely aligns with expert opinion.</p>
<p>“Breast density is an independent risk factor that drives how we communicate with women about their cancer risk. Our motivation was to create an accurate and consistent tool, that can be shared and used across health care systems,” says Adam Yala, a PhD student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and second author on a paper describing the model that was published today in <em>Radiology</em>.</p>
<p>The other co-authors are first author Constance Lehman, professor of radiology at Harvard Medical School and the director of breast imaging at the MGH; CSAIL PhD student Tal Schuster; Kyle Swanson '18, a CSAIL researcher and graduate student in the Department of Electrical Engineering and Computer Science; and senior author Regina Barzilay, the Delta Electronics Professor at CSAIL and the Department of Electrical Engineering and Computer Science at MIT and a member of the Koch Institute for Integrative Cancer Research at MIT.</p>
<p><strong>Mapping density</strong></p>
<p>The model is built on a convolutional neural network (CNN), which is also used for computer vision tasks. The researchers trained and tested their model on a dataset of more than 58,000 randomly selected mammograms from more than 39,000 women screened between 2009 and 2011. For training, they used around 41,000 mammograms and, for testing, about 8,600 mammograms.</p>
<p>Each mammogram in the dataset has a standard Breast Imaging Reporting and Data System (BI-RADS) breast density rating in four categories: fatty, scattered (scattered density), heterogeneous (mostly dense), and dense. In both training and testing mammograms, about 40 percent were assessed as heterogeneous and dense.</p>
<p>During the training process, the model is given random mammograms to analyze. It learns to map the mammogram with expert radiologist density ratings. Dense breasts, for instance, contain glandular and fibrous connective tissue, which appear as compact networks of thick white lines and solid white patches. Fatty tissue networks appear much thinner, with gray area throughout. In testing, the model observes new mammograms and predicts the most likely density category.</p>
<p><strong>Matching assessments</strong></p>
<p>The model was implemented at the breast imaging division at MGH. In a traditional workflow, when a mammogram is taken, it’s sent to a workstation for a radiologist to assess. The researchers’ model is installed in a separate machine that intercepts the scans before it reaches the radiologist, and assigns each mammogram a density rating. When radiologists pull up a scan at their workstations, they’ll see the model’s assigned rating, which they then accept or reject.</p>
<p>“It takes less than a second per image … [and it can be] easily and cheaply scaled throughout hospitals.” Yala says.</p>
<p>On over 10,000 mammograms at MGH from January to May of this year, the model achieved 94 percent agreement among the hospital’s radiologists in a binary test — determining whether breasts were either heterogeneous and dense, or fatty and scattered. Across all four BI-RADS categories, it matched radiologists’ assessments at 90 percent. “MGH is a top breast imaging center with high inter-radiologist agreement, and this high quality dataset enabled us to develop a strong model,” Yala says.</p>
<p>In general testing using the original dataset, the model matched the original human expert interpretations at 77 percent across four BI-RADS categories and, in binary tests, matched the interpretations at 87 percent.</p>
<p>In comparison with traditional prediction models, the researchers used a metric called a kappa score, where 1 indicates that predictions agree every time, and anything lower indicates fewer instances of agreements. Kappa scores for commercially available automatic density-assessment models score a maximum of about 0.6. In the clinical application, the researchers’ model scored 0.85 kappa score and, in testing, scored a 0.67. This means the model makes better predictions than traditional models.</p>
<p>In an additional experiment, the researchers tested the model’s agreement with consensus from five MGH radiologists from 500 random test mammograms. The radiologists assigned breast density to the mammograms without knowledge of the original assessment, or their peers’ or the model’s assessments. In this experiment, the model achieved a kappa score of 0.78 with the radiologist consensus.</p>
<p>Next, the researchers aim to scale the model into other hospitals. “Building on this translational experience, we will explore how to transition machine-learning algorithms developed at MIT into clinic benefiting millions of patients,” Barzilay says. “This is a charter of&nbsp;the new center at MIT — <a href="http://jclinic.mit.edu/">the Abdul Latif Jameel Clinic for Machine Learning in Health at MIT</a> — that was <a href="http://news.mit.edu/2018/abdul-latif-jameel-clinic-machine-learning-health-0917">recently launched</a>. And we are excited about new opportunities opened up by this center.”</p>
MIT and MGH researchers have developed an automated deep-learning model, currently being used on real patients, that identifies dense breast tissue — a risk factor for breast cancer — in mammograms as reliably as expert radiologists.Courtesy of the researchers Research, Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Artificial intelligence, Data, Health care, Health sciences and technology, Medicine, Cancer, Electrical Engineering & Computer Science (eecs), School of Engineering, J-Clinic, Koch InstituteProvost&#039;s letter to the faculty about the MIT Stephen A. Schwarzman College of Computinghttps://news.mit.edu/2018/provost-letter-faculty-mit-stephen-schwarzman-college-computing-1015
Mon, 15 Oct 2018 08:58:59 -0400MIT News Officehttps://news.mit.edu/2018/provost-letter-faculty-mit-stephen-schwarzman-college-computing-1015<p><em>The following email was sent today to the MIT faculty from Provost Martin Schmidt.</em></p>
<p>Dear colleagues,</p>
<p>As I trust you have seen, this morning Rafael wrote to the community to announce the creation of the MIT Stephen A. Schwarzman College of Computing. This is an historic day for the Institute.</p>
<p>The idea for the College emerged from a process of consultation the administration conducted over the past year. In that time, we consulted with many faculty members, both on School Councils and in some departments with significant computing activities. How to handle the explosive growth in student interest in computing, on its own and across other disciplines, has been an administrative concern for some time. As we’ve seen in the sharp rise in majors “with CS,” individual departments have worked hard to respond. But through more than a year’s worth of thoughtful input from many stakeholders, we came to see that if MIT could take a single bold step at scale, we could create important new opportunities for our community.</p>
<p>A central idea behind the College is that a new, shared structure can help deliver the power of computing, and especially AI, to all disciplines at MIT, lead to the development of new disciplines, and provide every discipline with an active channel to help shape the work of computing itself. Among those we have consulted so far, I sense a deep excitement for the power of this idea.</p>
<p><strong>Opportunities for input</strong></p>
<p>Today’s announcement has defined a vision for this College. Now, to realize its full potential, we are eager to launch a process that includes even more voices and perspectives. As a very first step, Rafael announced a set of community forums where we will share more detail on the vision and a process for moving forward. I hope you will join us for the <strong>faculty forum — October 18, 5:30–6:30 PM in 32-123 —</strong> so that we can learn from your feedback. The October 17th Faculty Meeting will also include discussion of the new College.</p>
<p><strong>The search for the Dean of the MIT Schwarzman College of Computing</strong></p>
<p>One immediate step is the search for the College’s inaugural dean. I am grateful to Institute Professor Ronald L. Rivest for agreeing to chair the search, and I am in the process of finalizing a search committee; we will announce the membership soon. I will ask the committee to recommend a short list of the best internal and external candidates by the end of November. It’s important that we work efficiently together to appoint a dean in the coming months, so that the new dean will be able to participle fully in implementing all aspects of the College.</p>
<p>I invite you to share your advice with the committee, including your suggestions for candidates for this important position, by sending email to <a href="mailto:CollegeOfComputingImplementation@mit.edu">CollegeOfComputingImplementation@mit.edu</a>. All correspondence will be kept confidential.</p>
<p><strong>The process moving forward</strong></p>
<p>The Chair of the Faculty Susan Silbey and I have discussed ideas for the best process moving forward. Even as we conduct a search for the new dean of the College, we can begin to make progress on several fronts.</p>
<p>At this point, we believe we could form a number of working groups to advise the administration on important details of creating the College, perhaps following the process MIT used during the 2008 budget crisis, which actively engaged all key stakeholders at the Institute. The working groups can evaluate options and make recommendations on issues like the detailed structure of the college, how faculty appointments will be made, and how we envision new degrees and instructional support that cut across the Institute. Again, we welcome your comments, questions, and insights as we move forward with this process. Please feel free to contribute any input via <a href="mailto:CollegeOfComputingImplementation@mit.edu">CollegeOfComputingImplementation@mit.edu</a>.</p>
<p>We have much work ahead of us, and I look forward to the excitement and challenge of writing this new chapter of the Institute’s history. I welcome your feedback and advice.</p>
<p>With my best regards,</p>
<p>Marty</p>
Provost, President L. Rafael Reif, Artificial intelligence, Machine learning, Administration, Faculty, Staff, Algorithms, Research, Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Quest for Intelligence, Technology and society, Giving, Ethics, Analytics, Classes and programs, MIT Schwarzman College of ComputingLetter to the MIT community regarding the MIT Stephen A. Schwarzman College of Computinghttps://news.mit.edu/2018/letter-mit-community-regarding-mit-stephen-schwarzman-college-computing
Mon, 15 Oct 2018 08:10:19 -0400MIT News Officehttps://news.mit.edu/2018/letter-mit-community-regarding-mit-stephen-schwarzman-college-computing<p><em>The following email was sent today to the MIT community by President L. Rafael Reif.</em></p>
<p>To the members of the MIT community,</p>
<p>The 2010 history, <em>Becoming MIT: Moments of Decision</em>, credits MIT’s record of rising impact to turning points when, responding to new challenges, MIT stayed true to its mission with a calculated change of course.</p>
<p>Today, at a turning point of equal consequence, we launch the MIT Stephen A. Schwarzman College of Computing.</p>
<p>This new College is our strategic response to a global phenomenon — the ubiquity of computing and the rise of AI. In this new world, we are building on MIT’s established leadership in these fields to position the Institute for decades to come as a world hub of education, research and innovation, and to prepare our students to lead in every domain.</p>
<p>To state the obvious, AI in particular is reshaping geopolitics, our economy, our daily lives and the very definition of work. It is rapidly enabling new research in every discipline and new solutions to daunting problems. At the same time, it is creating ethical strains and human consequences our society is not yet equipped to control or withstand.</p>
<p>In response, we are reshaping MIT.</p>
<p>By giving MIT’s five Schools a shared structure for collaborative education, research and innovation, the MIT Schwarzman College of Computing aims to:</p>
<ul>
<li>foster breakthroughs in computing, particularly artificial intelligence — actively informed by the wisdom of other disciplines;</li>
<li>deliver the power of AI tools to researchers in every field; and</li>
<li>advance pioneering work on AI’s ethical use and societal impact.</li>
</ul>
<p>Most distinctively, by adding new integrated curricula and degree programs in nearly every field, the College will equip students to be as fluent in computing and AI as they are in their own disciplines — and ready to use these digital tools wisely and humanely to help make a better world.</p>
<p>To be clear: In this pivotal AI moment, society has never needed the liberal arts — the path to wise, responsible citizenship — more than it does now. It is time to educate a new generation of technologists in the public interest.</p>
<p>You can read more about the vision for the MIT Schwarzman College of Computing <a href="http://news.mit.edu/2018/mit-reshapes-itself-stephen-schwarzman-college-of-computing-1015">here</a>, and you can find answers to questions of interest to faculty, students, staff and alumni <a href="http://news.mit.edu/2018/faq-mit-stephen-schwarzman-college-of-computing-1015">here</a>.</p>
<p><strong>How did the MIT Schwarzman College of Computing come to be?</strong></p>
<p>More than a year ago, inspired by the remarkable tide of student interest in majors with computing in the title, we began a process of assessment and exploration with the Executive Committee of the MIT Corporation. This quickly expanded to include faculty leadership in every department, including department heads, the School Councils and Academic Council. Faculty Chair Susan Silbey deserves immense credit for the nature and success of this consultative process. We have also gained key insights from Corporation members, students, staff and alumni. Together these conversations crystallized the need for bold action, at scale and with speed.</p>
<p>And so we arrived at the idea we announce as the MIT Schwarzman College of Computing — the most profound restructuring of MIT since the early 1950s. This $1 billion commitment will include a dedicated new building on campus, a new dean and a near doubling of our academic capability in computing and especially AI, with 50 new faculty positions located within the College and jointly with departments across MIT.</p>
<p>Such a bold step requires a bold partner. We are extremely fortunate to have the encouragement, insight and visionary support of one of the world’s most farsighted investors, Stephen A. Schwarzman, chairman, CEO and co-founder of Blackstone. His magnificent generosity — a gift of $350 million — gave us the power to take decisive action.</p>
<p><strong>What happens now?</strong></p>
<p>Both the MIT Corporation and its Executive Committee recently approved the establishment of the new College.</p>
<p>It is still, however, a very young idea ­— a prototype we are improving day by day. Its success will depend on thoughtful refinement and creative problem-solving from people across MIT. To jumpstart that feedback process, we have scheduled a number of forums:</p>
<p><strong>Faculty Forum</strong><br />
October 18, 5:30–6:30 PM<br />
Bldg. 32-123</p>
<p><strong>Student Forum</strong><br />
October 25, 5:00–6:00 PM<br />
Bldg. 32-123</p>
<p><strong>Staff Forum</strong><br />
October 25, Noon–1:00 PM<br />
Bldg. 4-270</p>
<p>In the coming days, we will schedule a forum for alumni in the metro-Boston area, as well as one or more webcasts&nbsp;to reach alumni in other regions and time zones.</p>
<p>Every forum will include lots of time for questions. To focus the conversation and guide our thinking, I hope that you will let us know here what questions interest or concern you the most.</p>
<p>In addition, faculty will receive an email from the Provost today describing the next steps in implementation and our search for a dean. The October 17th Faculty Meeting will also include discussion of the new College.</p>
<p class="rtecenter">*&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; *&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; *</p>
<p>As we begin this fresh chapter, I offer thanks to everyone who helped bring us to this day. For shepherding the development of this transformative idea, we owe special gratitude to Provost Marty Schmidt, Dean of Engineering Anantha Chandrakasan and Executive Vice President and Treasurer Israel Ruiz. &nbsp;</p>
<p>If we hope to make a better world, we must constantly work to make a better MIT. As humanity faces the opportunities and risks of the digital future, the reshaping we begin on campus today will challenge us to think deeply about how the technologies we invent can best serve, support and care for our global human family.</p>
<p>I look forward to joining you all in this profoundly important work.</p>
<p>In enthusiastic anticipation,</p>
<p>L. Rafael Reif</p>
President L. Rafael Reif, Artificial intelligence, Machine learning, Administration, Faculty, Staff, Algorithms, Research, Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Quest for Intelligence, Technology and society, Giving, MIT Schwarzman College of ComputingFAQ on the newly established MIT Stephen A. Schwarzman College of Computinghttps://news.mit.edu/2018/faq-mit-stephen-schwarzman-college-of-computing-1015
Mon, 15 Oct 2018 07:30:13 -0400MIT News Officehttps://news.mit.edu/2018/faq-mit-stephen-schwarzman-college-of-computing-1015<p><em>This set of FAQs offers information about the founding of the MIT Stephen A. Schwarzman College of Computing, <a href="http://news.mit.edu/2018/mit-reshapes-itself-stephen-schwarzman-college-of-computing-1015">announced today</a>, and its implications for the MIT community and beyond.</em></p>
<p><strong>General questions</strong></p>
<p><strong>Q: </strong>What is MIT announcing today that’s new?</p>
<p><strong>A:</strong> Today MIT is announcing a $1 billion commitment to address the global opportunities and challenges presented by the ubiquity of computing — across industries and academic disciplines — and by the rise of artificial intelligence. At the heart of this endeavor will be the new MIT Stephen A. Schwarzman College of Computing, made possible by a foundational $350 million gift from Stephen Schwarzman, the chairman, CEO, and co-founder of Blackstone, a leading global asset manager. An additional $300 million has been secured for the College through other fundraising.</p>
<p><strong>Q: </strong>Why is MIT creating this College?</p>
<p><strong>A:</strong> The Institute is creating the MIT Schwarzman College of Computing in response to clear trends both inside and outside MIT. Inside MIT, students are choosing in record numbers to study computer science, and departments across the Institute are creating joint majors with computer science and hiring faculty with expertise in computing. And externally, the digital fraction of the global economy has been growing much faster than the economy as a whole — and computing and AI are increasingly woven into every part of the global economy.</p>
<p><strong>Process and leadership</strong></p>
<p><strong>Q: </strong>What will implementation look like?</p>
<p><strong>A:</strong> MIT will launch a task force prior to the College’s opening in September 2019. The task force will make recommendations to the MIT administration on details regarding the structure of the College; its academic appointments and faculty recruiting; and — in particular — how best to structure the College such that there are seamless interactions in research and teaching between the College and other MIT departments.</p>
<p><strong>Q: </strong>When will the College’s first dean be appointed? Do you have a list of leading candidates?</p>
<p><strong>A:</strong> The Institute is finalizing a search advisory committee, charged by Provost Martin Schmidt, and is beginning the search process. The committee will move forward with appropriate speed and due diligence to ensure that MIT is ready to launch the College in September 2019.&nbsp;</p>
<p><strong>Q: </strong>Will the dean come from within MIT?</p>
<p><strong>A:</strong> MIT’s objective is to appoint the most highly qualified leader for this vitally important role. Such a leader may come from within MIT — but the best candidate may also come from outside the Institute. In support of the Institute and its mission, the dean will be responsible for ensuring the success of the College within the MIT community, across the broader MIT innovation ecosystem, and globally.&nbsp;</p>
<p><strong>Q: </strong>I’m an MIT community member. How can I learn more and offer thoughts?</p>
<p><strong>A:</strong> Both the MIT Corporation and its Executive Committee&nbsp;recently approved the establishment of the new College.&nbsp;But its success will depend on feedback from people across MIT. To jumpstart that process, the Institute has scheduled a number of forums:&nbsp;</p>
<p style="margin-left:.5in;"><strong>Faculty Forum</strong><br />
Thursday, October 18, 5:30-6:30 p.m.<br />
Room 32-123</p>
<p style="margin-left:.5in;"><strong>Student Forum</strong><br />
Thursday, October 25, 5:00-6:00 p.m.<br />
Room 32-123</p>
<p style="margin-left:.5in;"><strong>Staff Forum</strong><br />
Thursday, October 25, 12:00-1:00 p.m.<br />
Room 4-270</p>
<p>In the coming days, MIT will schedule a forum for alumni in the Boston area, as well as one or more webcasts to reach alumni in other regions and time zones. Every forum will include time for questions. To focus the conversations, members of the community are invited to email <u><a href="mailto:CollegeofComputingQuestions@mit.edu">CollegeofComputingQuestions@mit.edu</a></u> with questions or concerns.</p>
<p><strong>Impact on MIT</strong></p>
<p><strong>Q: </strong>Why is this a college, rather than a school? What is the difference?</p>
<p><strong>A:</strong> The MIT Schwarzman College of Computing will work with and across all five of MIT’s existing schools. Its naming as a college differentiates it from the five schools, and signals that it is an Institute-wide entity: The College is designed with cross-cutting education and research as its primary missions.</p>
<p><strong>Q: </strong>Why, and how, will the College connect to the schools and other parts of MIT?</p>
<p><strong>A:</strong> As MIT’s senior leaders have engaged with faculty and departments across campus, many have spoken of how their fields are being transformed by modern computational methods — specifically, by access to large data sets and the tools to learn from them. Some of the most exciting new work in fields like political science, economics, linguistics, anthropology, and urban studies — as well as in various disciplines in science and engineering — is being made possible when advanced computational capabilities are brought to these fields.</p>
<p>The key connector of the College to MIT’s five schools with be the 25 “bridge” faculty: joint faculty appointments linking the College with departments across MIT. With this new structure, MIT aims to educate students who are “bilingual” — adept in computing, as well as in their primary field. The College will also connect with the rest of MIT through its work to develop shared computing resources — infrastructure, instrumentation, and technical staffing.</p>
<p><strong>Q: </strong>Which existing MIT units will move into the College?</p>
<p><strong>A:</strong> It is expected that the Department of Electrical Engineering and Computer Science (EECS), the Computer Science and Artificial Intelligence Laboratory (CSAIL), the Institute for Data, Systems, and Society (IDSS), and the MIT Quest for Intelligence will all become part of the new College; other units may join the College. EECS (and in particular, the electrical engineering part of the department) will naturally continue to have a strong relationship with the School of Engineering, its current home. A set of faculty committees will be swiftly established to define the relationship between EECS, the School of Engineering, and the new College of Computing, as well as the range of future degree offerings.</p>
<p><strong>Q: </strong>What changes for MIT with this new College? Is this just a restructuring?</p>
<p><strong>A:</strong> The founding of the MIT Schwarzman College of Computing is the most significant structural change since 1950, when MIT established the Sloan School of Management and the School of Humanities, Arts, and Social Sciences. But this is much more than a restructuring: With this change, MIT seeks to position itself as a key player in the responsible and ethical evolution of technologies that will fundamentally transform society.</p>
<p>The College will reorient MIT to bring the power of computing and AI to all fields of study — and, in turn, to allow the future direction of computing and AI to be shaped by insights from all of these other disciplines, including the humanities. By design, the MIT Schwarzman College of Computing will be the connective tissue for the entire Institute, integrating AI studies and research with disciplines throughout MIT to a degree and with an intensity that, it is believed, is unmatched anywhere else.</p>
<p><strong>Q: </strong>The College has been described as a $1 billion endeavor. Where will that $1 billion come from, and how will it be spent?</p>
<p><strong>A:</strong> The estimated $1 billion cost to create the College will pay to construct a new building, expected to be complete around 2022; to create an endowment to support the 50 new faculty positions; and to fund computing resources to support teaching and research in the College and across MIT. The hiring of these new faculty, when complete in approximately five years, will represent a 5 percent growth in the Institute’s total faculty. Including the founding $350 million gift from Mr. Schwarzman, MIT has already secured 65 percent of the funds needed to support launch of the College.</p>
<p><strong>Q: </strong>How will this College impact MIT’s budget on an ongoing basis?</p>
<p><strong>A:</strong> A guiding principle of MIT’s planning is that the College should not dilute the resources of any other part of the Institute. This is why MIT is engaging in new fundraising to secure the remaining part of the estimated $1 billion needed to house the College and to endow its faculty.</p>
<p><strong>Impact on students and alumni</strong></p>
<p><strong>Q: </strong>Do you expect that this new structure could change the balance of undergraduate majors at MIT?</p>
<p><strong>A:</strong> About 40 percent of MIT undergraduates now major either in computer science alone or in joint programs combining computer science with some other field. It is expected that this new structure will allow interested students to gain a strong background in computer science while also focusing on a paired discipline that’s of greatest interest to them. By greatly expanding the range of disciplines that can be paired with computer science in a coherent undergraduate degree, this move will support MIT’s students in their clear desire to combine computer science with other fields where they might eventually apply their computing skills.</p>
<p><strong>Q: </strong>Will the undergraduate class size be increased?</p>
<p><strong>A:</strong> This remains to be determined. However, it is expected that the Institute’s population of graduate students will naturally grow with the addition of 50 new faculty positions.</p>
<p><strong>Q: </strong>Will current students be able to switch to the College?</p>
<p><strong>A:</strong> In general, MIT students are part of the school or college that is home to their academic program. Because the Department of Electrical Engineering and Computer Science (EECS) will become part of the new College, it is expected that the majority of EECS students will automatically become students within the new College. Students within MIT’s five other schools will, of course, be able to access the College’s faculty, courses, and facilities: Indeed, the College’s cross-Institute structure is intended to make it accessible to students across MIT, and there may be opportunities for students to be&nbsp;affiliated with both the College and their home department and school.</p>
<p><strong>Q: </strong>I'm a joint major in computer science and another discipline.&nbsp;How will this new College affect my course selection, and my degree?</p>
<p><strong>A:</strong> There should be no effect.</p>
<p><strong>Q: </strong>I’m an EECS alum. How will this new College affect my degree?</p>
<p><strong>A:</strong> You will continue to hold&nbsp;your MIT degree in your discipline.&nbsp;The creation of the College does not change your degree.&nbsp;This expanded footprint for computing at MIT is expected&nbsp;to enhance the stature of all&nbsp;computing-related fields&nbsp;at MIT.</p>
<p><strong>Impact on faculty</strong></p>
<p><strong>Q: </strong>How many new faculty positions will be created with the launch of the College?</p>
<p><strong>A:</strong> Fifty faculty positions will be added over the next five years. It’s expected that 25 of these faculty positions will be located fully within the new MIT Schwarzman College of Computing; the other 25 new faculty will hold “bridge” positions — dual appointments between the College and academic departments located in any of MIT’s five schools.</p>
<p><strong>Q: </strong>I’m a faculty member whose field has little connection to computing or AI. How will this new College affect my position at MIT?</p>
<p><strong>A:</strong> While MIT believes this new opportunity brings much possibility for all faculty, engagement with the new College will be entirely voluntary. Faculty who do not wish to engage more deeply with computing or AI will not be required to do so.</p>
<p><strong>Q: </strong>What kinds of new joint academic programs or degrees are envisioned?</p>
<p><strong>A:</strong> MIT has been making progress in this direction for some time; for example, we already offer undergraduate majors that pair computer science with economics, biology, mathematics, and urban planning. The MIT Schwarzman College of Computing will allow MIT to respond to the student demand the Institute is seeing in course and major/minor selection more effectively and creatively. It will enable MIT to pursue this vision with unprecedented depth and ambition, and will give MIT’s five schools a shared structure for collaborative education, research, and innovation in computing and AI.</p>
<p><strong>Impact on the physical campus</strong></p>
<p><strong>Q: </strong>What is the timeline on construction of a new building for the College? Where will the building be located? Has an architect been selected?</p>
<p><strong>A:</strong> The building is expected to be complete by 2022. Many details about the building, including its location on campus, have yet to be finalized. An architect has not been selected.</p>
<p><strong>Q: </strong>How big will the new building be?</p>
<p><strong>A:</strong> Given the expected growth of the MIT faculty with the launch of the MIT Schwarzman College of Computing, it is currently projected that the new building will house office and laboratory space for about 65 faculty members and their research groups and affiliated staff. This will likely translate to a building of 150,000 to 165,000 square feet. (For comparison purposes, MIT.nano is 200,000 square feet.)</p>
<p><strong>Q: </strong>Who will move into the new building?</p>
<p><strong>A:</strong> This remains to be determined. However, not all new MIT Schwarzman College of Computing faculty members will be in the new building, and it is expected that some existing faculty members will move there.</p>
<p><strong>The College’s focus</strong></p>
<p><strong>Q: </strong>AI encompasses a broad range of areas, from self-driving cars to robotics. Is MIT’s goal to be a leader in all the major AI areas? Are there specific areas the College will focus on?</p>
<p><strong>A:</strong> It is hoped and expected that the MIT Schwarzman College of Computing will become a convening force for all of the fields that center on computing and AI. However, the focus of the new College within these fields will be shaped largely by its first dean and by its academic leadership.</p>
<p><strong>Q: </strong>Will the new College partner with AI research companies?</p>
<p><strong>A:</strong> Numerous such companies are already part of MIT’s broader innovation ecosystem in Kendall Square, and the Institute will continue to collaborate with them. It is fair to assume that projects and research generated by the College will be of interest to industry, and will have commercial relevance. Additionally, it is expected that the “bilingual” graduates who emerge from this new College — combining competence in computing and in other fields — will be of enormous value to employers.</p>
<p><strong>Q: </strong>What ethical concerns does MIT have about AI or specific areas of AI research?</p>
<p><strong>A:</strong> Advances in computing, and artificial intelligence in particular, have the power to alter the fabric of society. The MIT Schwarzman College of Computing aims to be not only a center of advances in computing, but also a place for teaching and research on relevant policy and ethics — to better ensure that the pioneering technologies of the future are responsibly implemented in support of the greater good.</p>
<p><strong>Q: </strong>What kind of programs will there be around ethics and advances in computing?</p>
<p><strong>A:</strong> Launching the College will involve both an expansion of existing programs and the creation of entirely new ones — with some of these new programs exploring the intersection of ethics and computing. Within this space, the College will offer prestigious undergraduate research opportunities, graduate fellowships in ethics and AI, a seed-grant program for faculty, and a fellowship program to attract distinguished individuals from other universities, government, industry, and journalism.</p>
<p><strong>Q: </strong>Why is this focus on ethics important?</p>
<p><strong>A:</strong> Technologies reflect the values of those who make them. For this reason, technological advancements must be accompanied by the development of ethical guidelines that anticipate the risks of such enormously powerful innovations. MIT must make sure that the leaders who graduate from the Institute offer the world both technological proficiency and human wisdom — the cultural, ethical, and historical consciousness to use technology for the common good. MIT is founding the College, in part, to educate students in every discipline to responsibly use and develop AI and computing technologies to help make a better world.&nbsp;</p>
<p><strong>Q: </strong>At a time of growing economic disparities, there are deep concerns that AI will begin to replace humans and take over their jobs. How will MIT address such issues?</p>
<p><strong>A:</strong> AI and related technologies are poised to become a source of new wealth and industries. Together with that, however, is the risk of severe economic dislocation for individuals, communities, and entire nations. Reinventing the future of work must be a society-wide effort — and finding long-term solutions to issues arising from the deployment of AI will require ideas and initiative from every quarter.</p>
<p>The College will unite expertise at the intersection of computing and the society it serves. Joining scientists and engineers with social scientists, it will produce analysis of emerging technology; this research will serve industry, policymakers, and the broader research community. Some of the graduate students who conduct research in policy and ethics may go on to fill critical roles in government and at technology companies.</p>
<p>Additionally, MIT’s <a href="https://workofthefuture.mit.edu/">Task Force on the Work of the Future</a>, launched in February 2018, is an Institute-wide effort to understand and shape the evolution of jobs during the current age of innovation. It aims to shed new light on the linked evolution of technology and human work, and will issue findings guiding the development and implementation of policy, to suggest how society can continue to offer broad opportunity and prosperity.</p>
<p><strong>Q: </strong>Are there any AI areas in which MIT would not participate because of ethical concerns?&nbsp;</p>
<p><strong>A:</strong> Yes. In every action it takes, the Institute must understand whether its participation benefits society. Defining these boundaries will be the work of the College’s new leadership.</p>
President L. Rafael Reif, Artificial intelligence, Machine learning, Administration, Faculty, Staff, Algorithms, Research, Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Quest for Intelligence, Technology and society, Giving, Ethics, Analytics, Classes and programs, MIT Schwarzman College of ComputingMIT reshapes itself to shape the futurehttps://news.mit.edu/2018/mit-reshapes-itself-stephen-schwarzman-college-of-computing-1015
Gift of $350 million establishes the MIT Stephen A. Schwarzman College of Computing, an unprecedented, $1 billion commitment to world-changing breakthroughs and their ethical application.Mon, 15 Oct 2018 07:09:56 -0400MIT News Officehttps://news.mit.edu/2018/mit-reshapes-itself-stephen-schwarzman-college-of-computing-1015<p>MIT today announced a new $1 billion commitment to address the global opportunities and challenges presented by the prevalence of computing and the rise of artificial intelligence (AI). The initiative marks the single largest investment in computing and AI by an American academic institution, and will help position the United States to lead the world in preparing for the rapid evolution of computing and AI.</p>
<p>At the heart of this endeavor will be the new MIT Stephen A. Schwarzman College of Computing, made possible by a $350 million foundational gift from Mr. Schwarzman, the chairman, CEO and co-founder of Blackstone, a leading global asset manager.</p>
<p>Headquartered in a signature new building on MIT’s campus, the new MIT Schwarzman College of Computing will be an interdisciplinary hub for work in computer science, AI, data science, and related fields. The College will:</p>
<ul>
<li>reorient MIT to bring the power of computing and AI to all fields of study at MIT, allowing the future of computing and AI to be shaped by insights from all other disciplines;</li>
<li>create 50 new faculty positions that will be located both within the College and jointly with other departments across MIT — nearly doubling MIT’s academic capability in computing and AI;</li>
<li>give MIT’s five schools a shared structure for collaborative education, research, and innovation in computing and AI;</li>
<li>educate students in every discipline to responsibly use and develop AI and computing technologies to help make a better world; and</li>
<li>transform education and research in public policy and ethical considerations relevant to computing and AI.</li>
</ul>
<p>With the MIT Schwarzman College of Computing’s founding, MIT seeks to strengthen its position as a key international player in the responsible and ethical evolution of technologies that are poised to fundamentally transform society. Amid a rapidly evolving geopolitical environment that is constantly being reshaped by technology, the College will have significant impact on our nation’s competitiveness and security.</p>
<p>“As computing reshapes our world, MIT intends to help make sure it does so for the good of all,” says MIT President L. Rafael Reif. “In keeping with the scope of this challenge, we are reshaping MIT. The MIT Schwarzman College of Computing will constitute both a global center for computing research and education, and an intellectual foundry for powerful new AI tools. Just as important, the College will equip students and researchers in any discipline to use computing and AI to advance their disciplines and vice-versa, as well as to think critically about the human impact of their work. With uncommon insight and generosity, Mr. Schwarzman is enabling a bold agenda that will lead to a better world. I am deeply grateful for his commitment to our shared vision.”</p>
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<p>Stephen A. Schwarzman is chairman, CEO and co-founder of Blackstone, one of the world’s leading investment firms, with approximately $440 billion in assets under management. Mr. Schwarzman is an active philanthropist with a history of supporting education, culture, and the arts, among other things. Whether in business or philanthropy, he has dedicated himself to tackling global-scale problems, with transformative and paradigm-shifting solutions.</p>
<p>This year, he gave $5 million to Harvard Business School to support the development of case studies and other programming that explore the implications of AI on industries and business. In 2015, Mr. Schwarzman donated $150 million to Yale University to establish the Schwarzman Center, a first-of-its-kind campus center in Yale’s historic Commons building. In 2013, he founded a highly selective international scholarship program, Schwarzman Scholars, at Tsinghua University in Beijing to educate future global leaders about China. At $578 million raised to date, the program is modeled on the Rhodes Scholarship and is the single largest philanthropic effort in China’s history coming largely from international donors.</p>
<p>“There is no more important opportunity or challenge facing our nation than to responsibly harness the power of artificial intelligence so that we remain competitive globally and achieve breakthroughs that will improve our entire society,” Mr. Schwarzman says. “We face fundamental questions about how to ensure that technological advancements benefit all — especially those most vulnerable to the radical changes AI will inevitably bring to the nature of the workforce. MIT’s initiative will help America solve these challenges and continue to lead on computing and AI throughout the 21st century and beyond.”</p>
<p>“As one of the world leaders in technological innovation, MIT has the right expertise and the right values to serve as the ‘true north’ of AI in pursuit of the answers we urgently need,” Mr. Schwarzman adds. “With the ability to bring together the best minds in AI research, development, and ethics, higher education is uniquely situated to be the incubator for solving these challenges in ways the private and public sectors cannot. Our hope is that this ambitious initiative serves as a clarion call to our government that massive financial investment in AI is necessary to ensure that America has a leading voice in shaping the future of these powerful and transformative technologies.”</p>
<p><strong>New college, structure, building, and faculty</strong></p>
<p>The MIT Schwarzman College of Computing represents the most significant structural change to MIT since the early 1950s, which saw the establishment of schools for management and for the humanities and social sciences:</p>
<ul>
<li>The College is slated to open in Sept. 2019, with construction of a new building for the College scheduled to be completed in 2022.</li>
<li>Fifty new faculty positions will be created: 25 to be appointed to advance computing in the College, and 25 to be appointed jointly in the College and departments across MIT.</li>
<li>A new deanship will be established for the College.</li>
</ul>
<p>Today’s news follows a period of consultation of the MIT faculty led by President Reif, Provost Martin Schmidt, and Dean of the School of Engineering Anantha Chandrakasan. The chair of the faculty, Professor Susan Silbey, also participated in these consultations.&nbsp;Reif and Schmidt have also received letters of support for the College from academic leadership across MIT.</p>
<p>“Because the journey we embark on today will be Institute-wide, we needed input from across MIT in order to establish the right vision,” Schmidt says. “Our planning benefited greatly from the imagination of many members of our community — and we will seek a great deal more input over the next year. By design, the College will not be a silo: It will be connective tissue for the whole Institute.”</p>
<p>“I see exciting possibilities in this new structure,” says Melissa Nobles, dean of the MIT School of Humanities, Arts, and Social Sciences. “Faculty in a range of departments have a great deal to gain from new kinds of algorithmic tools — and a great deal of insight to offer their makers. Faculty in every school at MIT will be able to shape the work of the College.”</p>
<p>At its meeting on Oct. 5, the MIT Corporation — MIT’s board of trustees — endorsed the establishment of the College.</p>
<p>Corporation Chair Robert Millard says, “The new College positions MIT to lead in this important area, for the benefit of the United States and the world at large. In making this historic gift, Mr. Schwarzman has not only joined a select group of MIT’s most generous supporters, he has also helped give shape to a vision that will propel MIT into the future. We are all deeply grateful.”</p>
<p><strong>Empowering the pursuit of MIT’s mission</strong></p>
<p>The MIT Schwarzman College of Computing will aspire to excellence in MIT’s three main areas of work: education, research, and innovation:</p>
<ul>
<li>The College will teach students the foundations of computing broadly and provide integrated curricula designed to satisfy the high level of interest in majors that cross computer science with other disciplines, and in learning how machine learning and data science can be applied to a variety of fields.</li>
<li>It will seek to enable advances along the full spectrum of research — from fundamental, curiosity-driven inquiry to research on market-ready applications, in a wide range of MIT departments, labs, centers, and initiatives.</li>
</ul>
<p>“As MIT’s partner in shaping the future of AI, IBM is excited by this new initiative,” says Ginni Rometty IBM chairman, president, and CEO. “The establishment of the MIT Schwarzman College of Computing is an unprecedented investment in the promise of this technology. It will build powerfully on the pioneering research taking place through the MIT-IBM Watson AI Lab. Together, we will continue to unlock the massive potential of AI and explore its ethical and economic impacts on society.”</p>
<p><strong>Sparking thought around policy and ethics</strong></p>
<p>The MIT Schwarzman College of Computing will seek to be not only a center of advances in computing, but also a place for teaching and research on relevant policy and ethics to better ensure that the groundbreaking technologies of the future are responsibly implemented in support of the greater good. To advance these priorities, the College will:</p>
<ul>
<li>develop new curricula that will connect computer science and AI with other disciplines;</li>
<li>host forums to engage national leaders from business, government, academia, and journalism to examine the anticipated outcomes of advances in AI and machine learning, and to shape policies around the ethics of AI;</li>
<li>encourage scientists, engineers, and social scientists to collaborate on analysis of emerging technology, and on research that will serve industry, policymakers, and the broader research community; and</li>
<li>offer selective undergraduate research opportunities, graduate fellowships in ethics and AI, a seed-grant program for faculty, and a fellowship program to attract distinguished individuals from other universities, government, industry, and journalism.</li>
</ul>
<p>“Computing is no longer the domain of the experts alone. It’s everywhere, and it needs to be understood and mastered by almost everyone. In that context, for a host of reasons, society is uneasy about technology — and at MIT, that’s a signal we must take very seriously,” President Reif says. “Technological advancements must go hand in hand with the development of ethical guidelines that anticipate the risks of such enormously powerful innovations. This is why we must make sure that the leaders we graduate offer the world not only technological wizardry but also human wisdom — the cultural, ethical, and historical consciousness to use technology for the common good.”</p>
<p>“The College’s attention to ethics matters enormously to me, because we will never realize the full potential of these advancements unless they are guided by a shared understanding of their moral implications for society,” Mr. Schwarzman says. “Advances in computing — and in AI in particular — have increasing power to alter the fabric of society. But left unchecked, these technologies could ultimately hurt more people than they help. We need to do everything we can to ensure all Americans can share in AI’s development. Universities are best positioned for fostering an environment in which everyone can embrace — not fear — the transformations ahead.”</p>
<p>In its pursuit of ethical questions, the College will bring together researchers in a wide range of MIT departments, labs, centers, and initiatives, such as the Department of Electrical Engineering and Computer Science; the Computer Science and Artificial Intelligence Lab; the Institute for Data, Systems, and Society; the Operations Research Center; the Quest for Intelligence, and beyond.</p>
<p>“There is no doubt that artificial intelligence and automation will impact every facet of society. As we look to the future, we must utilize these important technologies to shape our world for the better and harness their power as a force for social good,” says Darren Walker, president of the Ford Foundation. “I believe that MIT’s groundbreaking initiative, particularly its commitment to address policy and ethics alongside technological advancements, will play a crucial role in ensuring that AI is developed responsibly and used to make our world more just.”</p>
<p><strong>Building on history and breadth</strong></p>
<p>The MIT Schwarzman College of Computing will build on MIT’s legacy of excellence in computation and the study of intelligence. In the 1950s, MIT Professor Marvin Minsky and others created the very idea of artificial intelligence:</p>
<ul>
<li>Today, Electrical Engineering and Computer Science (EECS) is by far the largest academic department at MIT. Forty percent of MIT’s most recent graduating class chose it, or a combination of it and another discipline, as their major. Its faculty boasts 10 of the 67 winners of the Turing Award, computing’s highest honor.</li>
<li>The largest laboratory at MIT is the Computer Science and Artificial Intelligence Laboratory, which was established in 2003 but has its roots in two pioneering MIT labs: the Artificial Intelligence Lab, established in 1959 to conduct pioneering research across a range of applications, and the Laboratory for Computer Science, established in 1963 to pursue a Department of Defense project for the development of a computer system accessible to a large number of people.</li>
<li>The College’s network function will rely on academic excellence across MIT. Outside of computer science and AI, the Institute hosts a high number of top-ranked departments, ready to be empowered by advances in these digital fields. <em>U.S. News and World Report</em> cites MIT as No. 1 in six graduate engineering specialties — and No. 1 in 17 disciplines and specialties outside of engineering, too, from biological sciences to economics.</li>
</ul>
<p>“A bold move to reshape the frontiers of computing is what you would expect from MIT,” says Eric Schmidt, former executive chairman of Alphabet and a visiting innovation fellow at MIT. “I’m especially excited about the MIT Schwarzman College of Computing, however, because it has such an obviously human agenda.” Schmidt also serves on the advisory boards of the MIT Quest for Intelligence and the MIT Work of the Future Task Force.</p>
<p>“We count many MIT graduates among our team at Apple, and have long admired how the school and its alumni approach technology with humanity in mind. MIT’s decision to focus on computing and AI across the entire institution shows tremendous foresight that will drive students and the world toward a better future,” says Apple CEO Tim Cook.</p>
<p><strong>The path forward</strong></p>
<p>On top of Mr. Schwarzman’s gift, MIT has raised an additional $300 million in support, totaling $650 million of the $1 billion required for the College. Further fundraising is being actively pursued by MIT’s senior administration.</p>
<p>Provost Schmidt has formed a committee to search for the College’s inaugural dean. He will also host forums in the coming days that will allow members of the MIT community to ask questions and offer suggestions about the College. The provost will work closely with the chair of the faculty and the dean of the School of Engineering to define the process for standing up the College.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>
<p>“I am truly excited by the work ahead,” Schmidt says. “The MIT community will give shape and energy to the College we launch today.”</p>
MIT will reshape itself to shape the future, investing $1 billion to address the rapid evolution of computing and AI — and its global effects. At the heart of this effort: a $350 million gift to found the MIT Stephen A. Schwarzman College of Computing.Photo: Christopher HartingPresident L. Rafael Reif, Artificial intelligence, Machine learning, Administration, Faculty, Staff, Algorithms, Research, Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Quest for Intelligence, Technology and society, Giving, Ethics, Analytics, Classes and programs, MIT Schwarzman College of ComputingCenter for Theoretical Physics professors earn DOE Quantum Information Science Awardshttps://news.mit.edu/2018/mit-center-theoretical-physics-professors-quantum-information-science-awards-1012
Professors Daniel Harlow, Aram Harrow, Hong Liu, and Jesse Thaler among the first recipients of new honor for advances in quantum understanding.Fri, 12 Oct 2018 16:20:00 -0400Scott Morley | Laboratory for Nuclear Sciencehttps://news.mit.edu/2018/mit-center-theoretical-physics-professors-quantum-information-science-awards-1012<p>Center for Theoretical Physics professors Daniel Harlow, Aram Harrow, Hong Liu and Jesse Thaler have been named&nbsp;recipients of research awards in the U.S. Department of Energy’s new program in Quantum Information Science (QIS).&nbsp;</p>
<p>The awards were made in conjunction with the White House Summit on Advancing American Leadership in QIS, highlighting the high priority that the current administration places on advancing this multidisciplinary area of research, which is expected to lay the foundation for the next generation of computing and information processing as well as an array of other innovative technologies.</p>
<p>The awards honor&nbsp;scientists at 28 institutions of higher learning across the nation and nine Department of Energy national laboratories. They cover a range of topics from developing hardware and software for a new generation of quantum computers, to the synthesis and characterization of new materials with special quantum properties, to probing the ways in which quantum computing and information processing provide insights into such cosmic phenomena as dark matter and black holes.</p>
<p>Harlow, Harrow, Liu, and Thaler, who are all researchers in the Laboratory for Nuclear Science and professors in the Department of Physics, are principles on two separate projects.&nbsp;&nbsp;</p>
<p>The first project, directed by Harlow, Harrow and Liu, will study connections between algebraic quantum field theory, holographic quantum codes, and approximate Markov states. These subjects have all been of much recent interest: The algebraic approach to quantum field theory has recently been used to prove remarkable general results such as the quantum null energy condition, and holographic quantum codes have given us a new perspective on classic problems in quantum gravity.</p>
<p>In both cases the technical tools which lead to the new results can be understood as using the special properties of quantum Markov states — states that saturate strong sub-additivity. These states are also of interest in quantum computing, with applications to quantum error-correction, efficient preparation of Gibbs states on quantum computers, efficient compression with side information, and many other areas. The project will combine these three issues, seeking to systematically understand the connections between them, seeking new insights about quantum field theory, quantum gravity, and quantum information.</p>
<p>“In recent years tools from quantum information theory have been very useful in studying the deep problems of quantum gravity, and I'm glad that the Department of Energy has recognized this,” Harlow says.&nbsp;“Hopefully continuing research in this direction will lead to additional insights, both about the fundamental structure of the universe and about what to do with quantum computers once we have them.”</p>
<p>In the second award, professors Harrow and Thaler were recognized for seeking to unite powerful analysis techniques in high energy physics with cutting-edge advances in quantum computation. Thaler’s research is aimed at discovering new physics at the Large Hadron Collider (LHC) and Harrow’s is directed at unlocking the capabilities of quantum computers. Through this innovative work at the interface of high-energy physics and quantum information science, the investigators aim to maximize the discovery potential of the LHC and future colliders by demonstrating how quantum algorithms can expose important features in collision events that would otherwise be intractable with classical methods.</p>
<p>By exploiting the capabilities of quantum computation, this research confronts the challenge of data analysis in collider physics and may pave the way for future applications of quantum machine learning beyond high energy physics, in particular clustering problems in other application domains.</p>
<p>“I'm excited to collaborate with experts in quantum information science,” says Thaler, speaking on the possibilities of the&nbsp;project.&nbsp;“While quantum mechanics is at the heart of particle physics, we currently rely on classical algorithms to analyze particle data.&nbsp;Quantum algorithms could enable discoveries at the LHC that would simply be intractable using classical methods.”</p>
<p>The work is funded by three programs in the Department of Energy's Office of Science: Advanced Scientific Computing Research, Basic Energy Sciences, and High Energy Physics.</p>
This illustration shows a multi-jet event recorded by the CMS detector at the Large Hadron Collider.Image courtesy of the Laboratory for Nuclear Science.School of Science, Physics, Quantum computing, Department of Energy (DoE), Awards, honors and fellowships, Faculty, Computer science and technology, Laboratory for Nuclear ScienceTaming “information hazards” in synthetic biology researchhttps://news.mit.edu/2018/taming-information-hazards-synthetic-biology-research-1004
Cryptography techniques to screen synthetic DNA could help prevent the creation of dangerous pathogens, argues Professor Kevin Esvelt.Thu, 04 Oct 2018 14:04:32 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/taming-information-hazards-synthetic-biology-research-1004<p><em>In 2016, synthetic biologists reconstructed a possibly extinct disease, known as horsepox, using mail-order DNA for around $100,000. The experiment was strictly for research purposes, and the disease itself is harmless to humans. But the published results, including the methodology, raised concerns that a nefarious agent, given appropriate resources, could engineer a pandemic. In an op-ed published today in </em>PLOS Pathogens<em>, Media Lab Professor Kevin Esvelt, who develops and studies gene-editing techniques, argues for tighter biosecurity and greater research transparency to keep such “information hazards” — published information that could be used to cause harm — in check. Esvelt spoke with </em>MIT News<em> about his ideas.</em></p>
<p><strong>Q: </strong>What are information hazards, and why are they an important topic in synthetic biology?</p>
<p><strong>A:</strong> Our society is not at ease with this notion that some information is hazardous, but it unfortunately happens to be true. No one believes the blueprints for nuclear weapons should be public, but we do collectively believe that the genome sequences for viruses should be public. This was not a problem until DNA synthesis got really good. The current system for regulating dangerous biological agents is bypassed by DNA synthesis. DNA synthesis is becoming accessible to a wide variety of people, and the instructions for doing nasty things are freely available online.</p>
<p>In the horsepox study, for instance, the information hazard is partly in the paper and the methods they described. But it’s also in the media covering it and highlighting that something bad can be done. And this is worsened by the people who are alarmed, because we talk to journalists about the potential harm, and that just feeds into it. As critics of these things, we are spreading information hazards too.</p>
<p>Part of the solution is just acknowledging that openness of information has costs, and taking steps to minimize those. That means raising awareness that information hazards exist, and being a little more cautious about talking about, and especially citing, dangerous work. Information hazards are a “tragedy of the commons” problem. Everyone thinks that, if it’s already out there, one more citation isn’t going to hurt. But everyone thinks that way. It just keeps on building until it’s on Wikipedia.</p>
<p><strong>Q: </strong>You say one issue with synthetic biology is screening DNA for potentially harmful sequences. How can cryptography help promote a market of “clean” DNA?</p>
<p><strong>A:</strong> We really need to do something about the ease of DNA synthesis and the accessibility of potential pandemic pathogens. The obvious solution is to get some kind of screening implemented for all DNA synthesis. The International Gene Synthesis Consortium (IGSC) was set up by industry leaders in DNA synthesis post-anthrax attacks. To be a member, a company needs to demonstrate it screens its orders, but member companies only cover 80 percent of the commercial market and none of the synthesis facilities within large firms. And there is no external way to verify that IGSC companies are actually doing the screening, or that they screen for the right things.</p>
<p>We need a more centralized system, where all DNA synthesis in the world is autonomously checked and would only be approved for synthesis if harmful sequences were not found in any of them. This is a cryptography problem.</p>
<p>On one hand, you have trade secrets, because firms making DNA don’t want others to know what they’re making. On the other hand, you have database of hazards that must be useless if stolen. You want to encrypt orders, send them to a centralized database, and then learn if it’s safe or not. Then you need a system for letting people add things to the database, which can be done privately. This is totally achievable with modern cryptography. You can use what’s known as hashes [which converts inputs of letters and numbers into an encrypted output of a fixed sequence] or do it using a newer method of fully homomorphic encryption, which lets you do calculations on encrypted data without ever decrypting it.</p>
<p>We’re just beginning to work on this challenge now. A point of this <em>PLOS</em> <em>Pathogens</em> op-ed is to lay the groundwork for this system.</p>
<p>In the long term, authorized experts can add hazards to their own databases. That’s the ideal way to deal with information hazards. If I think of a sequence that I’m confident is very dangerous, and people shouldn’t do this; ideally I would be able to contribute that to a database, possibly in conjunction with just one other authorized user who concurs. That could make sure nobody else makes that exact sequence, without unduly spreading the hazardous information of its identity and potential nature.</p>
<p><strong>Q: </strong>You argue for peer review during earlier research stages. How would that help prevent information hazards?</p>
<p><strong>A:</strong> The horsepox study was controversial with regard to whether the benefits outweighed the risks. It’s been said that one benefit was highlighting that viruses can be built from scratch. In oncological viral therapy, where you make viruses to kill cancer, [this information] could accelerate their research. It’s also been postulated that horsepox might be used to make a better vaccine, but that the researchers couldn’t access a sample. Those may be true. It’s still a clear information hazard. Could that aspect have been avoided?</p>
<p>Ideally, the horsepox study would have been reviewed by other experts, including some who were concerned by its implications and could have pointed out, for example, that you could have made a virus without harmful relatives as an example — or made horsepox, used it for vaccine development, and then just not specified that you made it from scratch. Then, you would have had all the research benefits of the study, without creating the information hazard. That would have been possible insofar as other experts had been given a chance to look at the research design before experiments were done.</p>
<p>With the current process, it’s typically only peer review at the end of the research. There’s no feedback at the research design phase at all. The time when peer review would be most useful would be at that phase. This transition requires funders, journals, and governments getting together to change [the process] in small subfields. In fields clearly without information hazards, you might publicly preregister your research plans and invite feedback. In fields like synthetic mammalian virology that present clear hazards, you’d want the research plans sent to a couple of peer reviewers in the field for evaluation, for safety and for suggested improvements. A lot of the time there’s a better way to do the experiment than you initially imagined, and if they can point that out at the beginning, then great. I think that both models will result in faster science, which we want too.</p>
<p>Universities could start by setting up a special process for early-stage peer review, internally, of gene drive [a genetic engineering technology] and mammalian virology experiments. As a scientist who works in both those fields, I would be happy to participate. The question is: How can we do [synthetic biology] in a way that continues or even accelerates beneficial discoveries while avoiding those with potentially catastrophic consequences?</p>
Kevin Esvelt directs the Sculpting Evolution Group at MIT Media LabImage: Maciek JasikResearch, Computer science and technology, Bioengineering and biotechnology, Synthetic biology, DNA, Data, cybersecurity, Media Lab, School of Architecture and Planning, Technology and societyModel helps robots navigate more like humans dohttps://news.mit.edu/2018/model-helps-robots-navigate-like-humans-1004
In simulations, robots move through new environments by exploring, observing, and drawing from learned experiences.Thu, 04 Oct 2018 00:00:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/model-helps-robots-navigate-like-humans-1004<p>When moving through a crowd to reach some end goal, humans can usually navigate the space safely without thinking too much. They can learn from the behavior of others and note any obstacles to avoid. Robots, on the other hand, struggle with such navigational concepts.</p>
<p>MIT researchers have now devised a way to help robots navigate environments more like humans do. Their novel motion-planning model lets robots determine how to reach a goal by exploring the environment, observing other agents, and exploiting what they’ve learned before in similar situations. A paper describing the model was presented at this week’s IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).</p>
<p>Popular motion-planning algorithms will create a tree of possible decisions that branches out until it finds good paths for navigation. A robot that needs to navigate a room to reach a door, for instance, will create a step-by-step search tree of possible movements and then execute the best path to the door, considering various constraints. One drawback, however, is these algorithms rarely learn: Robots can’t leverage information about how they or other agents acted previously in similar environments.</p>
<p>“Just like when playing chess, these decisions branch out until [the robots] find a good way to navigate. But unlike chess players, [the robots] explore what the future looks like without learning much about their environment and other agents,” says co-author Andrei Barbu, a researcher at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds, and Machines (CBMM) within MIT’s McGovern Institute. “The thousandth time they go through the same crowd is as complicated as the first time. They’re always exploring, rarely observing, and never using what’s happened in the past.”</p>
<p>The researchers developed a model that combines a planning algorithm with a neural network that learns to recognize paths that could lead to the best outcome, and uses that knowledge to guide the robot’s movement in an environment.</p>
<p>In their paper, “<a href="http://arxiv.org/abs/1810.00804" target="_blank">Deep sequential models for sampling-based planning</a>,” the researchers demonstrate the advantages of their model in two settings: navigating through challenging rooms with traps and narrow passages, and navigating areas while avoiding collisions with other agents. A promising real-world application is helping autonomous cars navigate intersections, where they have to quickly evaluate what others will do before merging into traffic. The researchers are currently pursuing such applications through the Toyota-CSAIL Joint Research Center.</p>
<p>“When humans interact with the world, we see an object we’ve interacted with before, or are in some location we’ve been to before, so we know how we’re going to act,” says Yen-Ling Kuo, a PhD student in CSAIL and first author on the paper. “The idea behind this work is to add to the search space a machine-learning model that knows from past experience how to make planning more efficient.”</p>
<p>Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL, is also a co-author on the paper.</p>
<p><strong>Trading off exploration and exploitation</strong></p>
<p>Traditional motion planners explore an environment by rapidly expanding a tree of decisions that eventually blankets an entire space. The robot then looks at the tree to find a way to reach the goal,&nbsp;such as a door. The researchers’ model, however, offers “a tradeoff between exploring the world and exploiting past knowledge,” Kuo says.</p>
<p>The learning process starts with a few examples. A robot using the model is trained on a few ways to navigate similar environments. The neural network learns what makes these examples succeed by interpreting the environment around the robot, such as the shape of the walls, the actions of other agents, and features of the goals. In short, the model “learns that when you’re stuck in an environment, and you see a doorway, it’s probably a good idea to go through the door to get out,” Barbu says.</p>
<p>The model combines the exploration behavior from earlier methods with this learned information. The underlying planner, called RRT*, was developed by MIT professors Sertac Karaman and Emilio Frazzoli. (It’s a variant of a widely used motion-planning algorithm known as Rapidly-exploring Random Trees, or&nbsp; RRT.) The planner creates a search tree while the neural network mirrors each step and makes probabilistic predictions about where the robot should go next. When the network makes a prediction with high confidence, based on learned information, it guides the robot on a new path. If the network doesn’t have high confidence, it lets the robot explore the environment instead, like a traditional planner.</p>
<p>For example, the researchers demonstrated the model in a simulation known as a “bug trap,” where a 2-D robot must escape from an inner chamber through a central narrow channel and reach a location in a surrounding larger room. Blind allies on either side of the channel can get robots stuck. In this simulation, the robot was trained on a few examples of how to escape different bug traps. When faced with a new trap, it recognizes features of the trap, escapes, and continues to search for its goal in the larger room. The neural network helps the robot find the exit to the trap, identify the dead ends, and gives the robot a sense of its surroundings so it can quickly find the goal.</p>
<p>Results in the paper are based on the chances that a path is found after some time, total length of the path that reached a given goal, and how consistent the paths were. In both simulations, the researchers’ model more quickly plotted far shorter and consistent paths than a traditional planner.</p>
<p>“This model is interesting because it allows a motion planner to adapt&nbsp;to what it sees in the environment,” says Stephanie Tellex, an assistant professor of computer science at Brown University, who was not involved in the research. “This can enable dramatic&nbsp;improvements in planning speed by customizing the planner to what the robot knows. Most planners don't adapt to the environment at all. Being able to&nbsp;traverse long, narrow passages is notoriously difficult for a&nbsp;conventional planner, but they can solve it. We need more ways that&nbsp;bridge this gap.”</p>
<p><strong>Working with multiple agents</strong></p>
<p>In one other experiment, the researchers trained and tested the model in navigating environments with multiple moving agents, which is a useful test for autonomous cars, especially navigating intersections and roundabouts. In the simulation, several agents are circling an obstacle. A robot agent must successfully navigate around the other agents, avoid collisions, and reach a goal location, such as an exit on a roundabout.</p>
<p>“Situations like roundabouts are hard, because they require reasoning about how others will respond to your actions, how you will then respond to theirs, what they will do next, and so on,” Barbu says. “You eventually discover your first action was wrong, because later on it will lead to a likely accident. This problem gets exponentially worse the more cars you have to contend with.”</p>
<p>Results indicate that the researchers’ model can capture enough information about the future behavior of the other agents (cars) to cut off the process early, while still making good decisions in navigation. This makes planning more efficient. Moreover, they only needed to train the model on a few examples of roundabouts with only a few cars. “The plans the robots make take into account what the other cars are going to do, as any human would,” Barbu says.</p>
<p>Going through intersections or roundabouts is one of the most challenging scenarios facing autonomous cars. This work might one day let cars learn how humans behave and how to adapt to drivers in different environments, according to the researchers. This is the focus of the Toyota-CSAIL Joint Research Center work.</p>
<p>“Not everybody behaves the same way, but people are very stereotypical. There are people who are shy, people who are aggressive. The model recognizes that quickly and that’s why it can plan efficiently,” Barbu says.</p>
<p>More recently, the researchers have been applying this work to robots with manipulators that face similarly daunting challenges when reaching for objects in ever-changing environments.</p>
MIT researchers have devised a way to help robots navigate environments more like humans do.Research, Computer science and technology, Robotics, Algorithms, Artificial intelligence, Machine learning, Autonomous vehicles, Computer Science and Artificial Intelligence Laboratory (CSAIL), Center for Brains Minds and Machines, McGovern Institute, Electrical Engineering & Computer Science (eecs), School of EngineeringDetecting fake news at its sourcehttps://news.mit.edu/2018/mit-csail-machine-learning-system-detects-fake-news-from-source-1004
Machine learning system aims to determine if an information outlet is accurate or biased.Thu, 04 Oct 2018 00:00:00 -0400Adam Conner-Simons | CSAILhttps://news.mit.edu/2018/mit-csail-machine-learning-system-detects-fake-news-from-source-1004<p>Lately the fact-checking world has been in a bit of a crisis. Sites like Politifact and Snopes have traditionally focused on specific claims, which is admirable but tedious; by the time they’ve gotten through verifying or debunking a fact, there’s a good chance it’s already traveled across the globe and back again.</p>
<p>Social media companies have also had mixed results limiting the spread of propaganda and misinformation.&nbsp;Facebook plans to have <a href="http://fortune.com/2018/03/22/human-moderators-facebook-youtube-twitter/">20,000 human moderators</a> by the end of the year, and is putting significant resources into developing its own <a href="https://thenextweb.com/artificial-intelligence/2018/07/02/facebook-just-bought-an-ai-startup-to-help-it-fight-fake-news/">fake-news-detecting algorithms</a>.</p>
<p>Researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and the Qatar Computing Research Institute (QCRI) believe that the best approach is to focus not only on individual claims, but on the news sources themselves. Using this tack, they’ve demonstrated a new system that uses machine learning to determine if a source is accurate or politically biased.</p>
<p>“If a website has published fake news before, there’s a good chance they’ll do it again,” says postdoc Ramy Baly, the lead author on <a href="https://arxiv.org/pdf/1810.01765.pdf" target="_blank">a new paper</a> about the system. “By automatically scraping data about these sites, the hope is that our system can help figure out which ones are likely to do it in the first place.”</p>
<p>Baly says the system needs only about 150 articles to reliably detect if a news source can be trusted —&nbsp;meaning that an approach like theirs could be used to help stamp out new fake-news outlets before the stories spread too widely.</p>
<p>The system is a collaboration between computer scientists at MIT CSAIL and QCRI, which is part of the Hamad Bin Khalifa University in Qatar. Researchers first took data from <a href="https://mediabiasfactcheck.com/">Media Bias/Fact Check</a> (MBFC), a website with human fact-checkers who analyze the accuracy and biases of more than 2,000 news sites; from MSNBC and Fox News; and from low-traffic content farms.</p>
<p>They then fed those data to a machine learning algorithm, and programmed it to classify news sites the same way as MBFC. When given a new news outlet, the system was then 65 percent accurate at detecting whether it has a high, low or medium level of factuality,&nbsp;and roughly 70 percent accurate at detecting if it is left-leaning, right-leaning, or moderate.</p>
<p>The team determined that the most reliable ways to detect both fake news and biased reporting were to look at the common linguistic features across the source’s stories, including sentiment, complexity, and structure.</p>
<p>For example, fake-news outlets were found to be more likely to use language that is hyperbolic, subjective, and emotional. In terms of bias, left-leaning outlets were more likely to have language that related to concepts of harm/care and fairness/reciprocity, compared to other qualities such as loyalty, authority, and sanctity. (These qualities represent&nbsp;a popular theory — that there are&nbsp;five major moral foundations — in social psychology.)</p>
<p>Co-author Preslav Nakov, a senior scientist at QCRI, says that the system also found correlations with an outlet’s Wikipedia page, which it assessed for general —&nbsp;longer is more credible —&nbsp;as well as target words such as&nbsp;“extreme” or &nbsp;“conspiracy theory.” It even found correlations with the text structure of a source’s URLs: Those that had lots of special characters and complicated subdirectories, for example, were associated with less reliable sources.</p>
<p>“Since it is much easier to obtain ground truth on sources [than on articles], this method is able to provide direct and accurate predictions regarding the type of content distributed by these sources,” says Sibel Adali, a professor of computer science at Rensselaer Polytechnic Institute who was not involved in the project.<br />
&nbsp;<br />
Nakov is quick to caution that the system is still a work in progress, and that, even with improvements in accuracy, it would work best in conjunction with traditional fact-checkers.</p>
<p>“If outlets report differently on a particular topic, a site like Politifact could instantly look at our fake news&nbsp;scores for those outlets to determine how much validity to give to different perspectives,” says Nakov.</p>
<p>Baly and Nakov co-wrote the new paper with MIT Senior Research Scientist James Glass alongside graduate students Dimitar Alexandrov and Georgi Karadzhov of Sofia University. The team will present the work later this month at the 2018 Empirical Methods in Natural Language Processing (EMNLP) conference in Brussels, Belgium.</p>
<p>The researchers also created a new open-source dataset of more than 1,000 news sources, annotated with factuality and bias scores, that is&nbsp;the world’s largest database of its kind. As next steps, the team will be exploring whether the English-trained system can be adapted to other languages, as well as to go beyond the traditional left/right bias to explore region-specific biases (like the Muslim world’s division between religious and secular).</p>
<p>“This direction of research can shed light on what untrustworthy websites look like and the kind of content they tend to share, which would be very useful for both web designers and the wider public,” says Andreas Vlachos, a senior lecturer at the University of Cambridge who was not involved in the project.</p>
<p>Nakov says that QCRI also has plans to roll out an app that helps users step out of their political bubbles, responding to specific news items by offering users a collection of articles that span the political spectrum.</p>
<p>“It’s interesting to think about new ways to present the news to people,” says Nakov. “Tools like this could help people give a bit more thought to issues and explore other perspectives that they might not have otherwise considered."</p>
According to a system developed at MIT, the most reliable way to detect fake news and biased reporting is to look at the common linguistic features across the source’s stories, including sentiment, complexity, and structure.Image courtesy of MIT CSAILSchool of Engineering, Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Politics, Social media, Research, Technology and society, Electrical Engineering & Computer Science (eecs), WritingReport outlines keys to election securityhttps://news.mit.edu/2018/report-keys-election-security-0925
MIT experts are among co-authors calling for ballot paper trails and other resilient practices to avoid election hacking. Mon, 24 Sep 2018 23:59:59 -0400Peter Dizikes | MIT News Officehttps://news.mit.edu/2018/report-keys-election-security-0925<p>The most secure form of voting technology remains the familiar, durable innovation known as paper, according to <a href="https://www.nap.edu/catalog/25120/securing-the-vote-protecting-american-democracy">a report</a> authored by a group of election experts, including two prominent scholars from MIT.</p>
<p>The report, issued by the National Academies of Science, Engineering, and Medicine, is a response to the emerging threat of hackers targeting computerized voting systems, and it comes as concerns continue to be aired over the security of the U.S. midterm elections of 2018.</p>
<p>The U.S. has a decentralized voting system, with roughly 9,000 political jurisdictions bearing some responsibility for administering elections. However, for all that variation, and while many questions are swirling around election security, the report identifies some main themes on the topic.</p>
<p>“There are two really important avenues that are emerging,” says Charles Stewart, the Kenan Sahin Distinguished Professor of Political Science and founder of MIT’s Election Data and Science Lab. “One is just securing the election, and the other is building in resilience and fail-safe mechanisms.”</p>
<p>In this context, “securing the election” means keeping voting systems safe from hackers in the first place; fail-safe mechanisms include paper ballots that can be used for audits and recounts.</p>
<p>The other MIT co-author of the report is Ronald L. Rivest, a computer encryption pioneer and Institute Professor in the Department of Electrical Engineering and Computer Science. Given the distinct challenges of combining anonymity at the ballot box with verification of voting, Rivest notes, a paper trail remains a necessary component of secure voting systems.</p>
<p>“I think that the three most important recommendations of the report, at least from a security perspective, are probably: (a) use paper ballots, (b) check the reported election outcomes by performing ‘risk-limiting audits’ of the cast paper ballots, and (c) don’t transmit cast votes over the internet,” Rivest says.</p>
<p>The report, “Securing the Vote: Protecting American Democracy,” was released this month by the National Academies. The co-chairs of the committee releasing the report are Lee C. Bollinger, president of Columbia University, and Michael A. McRobbie, president of Indiana University.</p>
<p>Rivest and Stewart are two of the 12 co-authors of the high-level report, which examines a range of voting issues and contains a series of recommendations. In addition to having a paper trail, the recommendations include securing and updating voter registration databases, robust checks on the security of voting by mail, Congressional funding for security standards developed by the National Institute of Standards and Technology and the U.S. Election Assistance Commission, and robust auditing of elections to make sure systems are working.</p>
<p>Stewart and Rivest both acknowledge that they are often asked why internet voting is not a reality, given that we conduct other kinds of sensitive activities online, including banking.</p>
<p>“Probably the most common question that I get when I talk to the public about these issues,” Stewart says, “is, ‘Why can’t we vote on the internet?’”</p>
<p>Systems with the right combination of verification and anonymity are hard to develop, however, and as both scholars point out, other online activities such as banking are hardly foolproof. And while banks have systems to compensate customers should fraud occur, a one-time event like an election does not provide the same opportunities for remedies.</p>
<p>The good news, Stewart suggests, is that election officials themselves tend to have a keen awareness of the best practices in their field.</p>
<p>“From my experience I know that every state election official and just about every local election official that I’ve talked to is aware that cybersecurity is a top priority,” Stewart says. However, he adds, election officials do not necessarily control the purse strings and often cannot fund the security measures they value: “Often times, election officials don’t have control over their own destiny.”</p>
With the U.S. midterm elections approaching, a new report on keeping voting systems safe from hackers was co-authored by MIT professors Ronald L. Rivest (left) and Charles Stewart III.Courtesy of Charles Stewart and Ronald RivestSchool of Humanities Arts and Social Sciences, School of Engineering, Politics, Elections, cybersecurity, Computer science and technology, Technology and society, Computer Science & Electrical Engineering (eecs), Political science, GovernmentReducing false positives in credit card fraud detectionhttps://news.mit.edu/2018/machine-learning-financial-credit-card-fraud-0920
Model extracts granular behavioral patterns from transaction data to more accurately flag suspicious activity.Thu, 20 Sep 2018 00:00:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/machine-learning-financial-credit-card-fraud-0920<p>Have you ever used your credit card at a new store or location only to have it declined? Has a sale ever been blocked because you charged a higher amount than usual?</p>
<p>Consumers’ credit cards are declined surprisingly often in legitimate transactions. One cause is that fraud-detecting technologies used by a consumer’s bank have incorrectly flagged the sale as suspicious. Now MIT researchers have employed a new machine-learning technique to drastically reduce these false positives, saving banks money and easing customer frustration.</p>
<p>Using machine learning to detect financial fraud dates back to the early 1990s and has advanced over the years. Researchers train models to extract behavioral patterns from past transactions, called “features,” that signal fraud. When you swipe your card, the card pings the model and, if the features match fraud behavior, the sale gets blocked.</p>
<p>Behind the scenes, however, data scientists must dream up those features, which mostly center on blanket rules for amount and location. If any given customer spends more than, say, $2,000 on one purchase, or makes numerous purchases in the same day, they may be flagged. But because consumer spending habits vary, even in individual accounts, these models are sometime inaccurate: A 2015 report from Javelin Strategy and Research estimates that only one in five fraud predictions is correct and that the errors can cost a bank $118 billion in lost revenue, as declined customers then refrain from using that credit card.</p>
<p>The MIT researchers have developed an “automated feature engineering” approach that &nbsp;extracts more than 200 detailed features for each individual transaction — say, if a user was present during purchases, and the average amount spent on certain days at certain vendors. By doing so, it can better pinpoint when a specific card holder’s spending habits deviate from the norm.</p>
<p>Tested on a dataset of 1.8 million transactions from a large bank, the model&nbsp;reduced false positive predictions by 54 percent over traditional models, which the researchers estimate could have saved the bank 190,000 euros (around $220,000) in lost revenue.</p>
<p>“The big challenge in this industry is false positives,” says Kalyan Veeramachaneni, a principal research scientist at MIT’s Laboratory for Information and Decision Systems (LIDS) and co-author of a paper describing the model, which was presented at the recent European Conference for Machine Learning. “We can say there’s a direct connection between feature engineering and [reducing] false positives. … That’s the most impactful thing to improve accuracy of these machine-learning models.”</p>
<p>Paper co-authors include: lead author Roy Wedge '15, a former researcher in the Data to AI Lab at LIDS; James Max Kanter ’15, SM ’15; and Sergio Iglesias Perez of Banco Bilbao Vizcaya Argentaria.</p>
<p><strong>Extracting “deep” features</strong></p>
<p>Three years ago, Veeramachaneni and Kanter developed Deep Feature Synthesis (DFS), an automated approach that extracts highly detailed features from any data, and decided to apply it to financial transactions.</p>
<p>Enterprises will sometimes host competitions where they provide a limited dataset along with a prediction problem such as fraud. Data scientists develop prediction models, and a cash prize goes to the most accurate model. The researchers entered one such competition and achieved top scores with DFS.</p>
<p>However, they realized the approach could reach its full potential if trained on several sources of raw data. “If you look at what data companies release, it’s a tiny sliver of what they actually have,” Veeramachaneni says. “Our question was, ‘How do we take this approach to actual businesses?’”</p>
<p>Backed by the Defense Advanced Research Projects Agency’s Data-Driven Discovery of Models program, Kanter and his team at <a href="http://www.featurelabs.com/">Feature Labs</a> — a spinout commercializing the technology — developed an open-source library for automated feature extraction, called <a href="http://www.featuretools.com/">Featuretools</a>, which was used in this research.</p>
<p>The researchers obtained a three-year dataset provided by an international bank, which included granular information about transaction amount, times, locations, vendor types, and terminals used. It contained about 900 million transactions from around 7 million individual cards. Of those transactions, around 122,000 were confirmed as fraud. The researchers trained and tested their model on subsets of that data.</p>
<p>In training, the model looks for patterns of transactions and among cards that match cases of fraud. It then automatically combines all the different variables it finds into “deep” features that provide a highly detailed look at each transaction. From the dataset, the DFS model extracted 237 features for each transaction. Those represent highly customized variables for card holders, Veeramachaneni says. “Say, on Friday, it’s usual for a customer to spend $5 or $15 dollars at Starbucks,” he says. “That variable will look like, ‘How much money was spent in a coffee shop on a Friday morning?’”</p>
<p>It then creates an if/then decision tree for that account of features that do and don’t point to fraud. When a new transaction is run through the decision tree, the model decides in real time whether or not the transaction is fraudulent.</p>
<p>Pitted against a traditional model used by a bank, the DFS model generated around 133,000 false positives versus 289,000 false positives, about 54 percent fewer incidents. That, along with a smaller number of false negatives detected — actual fraud that wasn’t detected — could save the bank an estimated 190,000 euros, the researchers estimate.</p>
<p>Iglesias&nbsp;notes he and his colleagues at BBVA have consistently been able to reproduce the MIT team’s results using the DFS model with additional card and business data, with a minimum increase in computational cost.&nbsp;</p>
<p><strong>Stacking primitives</strong></p>
<p>The backbone of the model consists of creatively stacked “primitives,” simple functions that take two inputs and give an output. For example, calculating an average of two numbers is one primitive. That can be combined with a primitive that looks at the time stamp of two transactions to get an average time between transactions. Stacking another primitive that calculates the distance between two addresses from those transactions gives an average time between two purchases at two specific locations. Another primitive could determine if the purchase was made on a weekday or weekend, and so on.</p>
<p>“Once we have those primitives, there is no stopping us for stacking them … and you start to see these interesting variables you didn’t think of before. If you dig deep into the algorithm, primitives are the secret sauce,” Veeramachaneni says.</p>
<p>One important feature that the model generates, Veeramachaneni notes, is calculating the distance between those two locations and whether they happened in person or remotely. If someone who buys something at, say, the Stata Center in person and, a half hour later, buys something in person 200 miles away, then it’s a high probability of fraud. But if one purchase occurred through mobile phone, the fraud probability drops.</p>
<p>“There are so many features you can extract that characterize behaviors you see in past data that relate to fraud or nonfraud use cases,” Veeramachaneni says.</p>
<p>"In fact, this automated feature synthesis technique, and the overall knowledge provided by MIT in this project, has shown us a new way of refocusing research in other challenges in which we initially have a reduced set of features. For example, we are obtaining equally promising results in the detection of anomalous behavior in internal network traffic or in market operations, just to mention two [examples],” Iglesias adds.</p>
MIT researchers have employed a new machine-learning technique to substantially reduce false positives in fraud-detecting technologies.Image: Chelsea TurnerResearch, Computer science and technology, Data, Finance, Industry, Innovation and Entrepreneurship (I&E), Startups, Machine learning, Algorithms, Artificial intelligence, Behavior, Department of Electrical Engineering and Computer Science (EECS), School of EngineeringMachine-learning system tackles speech and object recognition, all at oncehttps://news.mit.edu/machine-learning-image-object-recognition-0918
Model learns to pick out objects within an image, using spoken descriptions.Tue, 18 Sep 2018 00:00:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/machine-learning-image-object-recognition-0918<p>MIT computer scientists have developed a system that learns to identify objects within an image, based on a spoken description of the image. Given an image and an audio caption, the model will highlight in real-time the relevant regions of the image being described.</p>
<p>Unlike current speech-recognition technologies, the model doesn’t require manual transcriptions and annotations of the examples it’s trained on. Instead, it learns words directly from recorded speech clips and objects in raw images, and associates them with one another.</p>
<p>The model can currently recognize only several hundred different words and object types. But the researchers hope that one day their combined speech-object recognition technique could save countless hours of manual labor and open new doors in speech and image recognition.</p>
<p>Speech-recognition systems such as Siri, for instance, require transcriptions of many thousands of hours of speech recordings. Using these data, the systems learn to map speech signals with specific words. Such an approach becomes especially problematic when, say, new terms enter our lexicon, and the systems must be retrained.</p>
<p>“We wanted to do speech recognition in a way that’s more natural, leveraging additional signals and information that humans have the benefit of using, but that machine learning algorithms don’t typically have access to. We got the idea of training a model in a manner similar to walking a child through the world and narrating what you’re seeing,” says David Harwath, a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Spoken Language Systems Group. Harwath co-authored a paper describing the model that was presented at the recent European Conference on Computer Vision.</p>
<p>In the paper, the researchers demonstrate their model on an image of a young girl with blonde hair and blue eyes, wearing a blue dress, with a white lighthouse with a red roof in the background. The model learned to associate which pixels in the image corresponded with the words “girl,” “blonde hair,” “blue eyes,” “blue dress,” “white light house,” and “red roof.” When an audio caption was narrated, the model then highlighted each of those objects in the image as they were described.</p>
<p>One promising application is learning translations between different languages, without need of a bilingual annotator. Of the estimated 7,000 languages spoken worldwide, only 100 or so have enough transcription data for speech recognition. Consider, however, a situation where two different-language speakers describe the same image. If the model learns speech signals from language A that correspond to objects in the image, and learns the signals in language B that correspond to those same objects, it could assume those two signals — and matching words — are translations of one another.</p>
<p>“There’s potential there for a Babel Fish-type of mechanism,” Harwath says, referring to the fictitious living earpiece in the “Hitchhiker’s Guide to the Galaxy” novels that translates different languages to the wearer.</p>
<p>The CSAIL co-authors are: graduate student Adria Recasens; visiting student Didac Suris; former researcher Galen Chuang; Antonio Torralba, a professor of electrical engineering and computer science who also heads the MIT-IBM Watson AI Lab; and Senior Research Scientist James Glass, who leads the Spoken Language Systems Group at CSAIL.</p>
<p><strong>Audio-visual associations</strong></p>
<p>This work expands on an earlier model developed by Harwath, Glass, and Torralba that correlates speech with groups of thematically related images. In the earlier research, they put images of scenes from a classification <a href="http://places.csail.mit.edu/">database</a> on the crowdsourcing Mechanical Turk platform. They then had people describe the images as if they were narrating to a child, for about 10 seconds. They compiled more than 200,000 pairs of images and audio captions, in hundreds of different categories, such as beaches, shopping malls, city streets, and bedrooms.</p>
<p>They then designed a model consisting of two separate convolutional neural networks (CNNs). One processes images, and one processes spectrograms, a visual representation of audio signals as they vary over time. The highest layer of the model computes outputs of the two networks and maps the speech patterns with image data.</p>
<p>The researchers would, for instance, feed the model caption A and image A, which is correct. Then, they would feed it a random caption B with image A, which is an incorrect pairing. After comparing thousands of wrong captions with image A, the model learns the speech signals corresponding with image A, and associates those signals with words in the captions. As described in a 2016 <a href="http://news.mit.edu/2016/recorded-speech-images-automated-speech-recognition-1206">study</a>, the model learned, for instance, to pick out the signal corresponding to the word “water,” and to retrieve images with bodies of water.</p>
<p>“But it didn’t provide a way to say, ‘This is exact point in time that somebody said a specific word that refers to that specific patch of pixels,’” Harwath says.</p>
<p><strong>Making a matchmap</strong></p>
<p>In the new paper, the researchers modified the model to associate specific words with specific patches of pixels. The researchers trained the model on the same database, but with a new total of 400,000 image-captions pairs. They held out 1,000 random pairs for testing.</p>
<p>In training, the model is similarly given correct and incorrect images and captions. But this time, the image-analyzing CNN divides the image into a grid of cells consisting of patches of pixels. The audio-analyzing CNN divides the spectrogram into segments of, say, one second to capture a word or two.</p>
<p>With the correct image and caption pair, the model matches the first cell of the grid to the first segment of audio, then matches that same cell with the second segment of audio, and so on, all the way through each grid cell and across all time segments. For each cell and audio segment, it provides a similarity score, depending on how closely the signal corresponds to the object.</p>
<p>The challenge is that, during training, the model doesn’t have access to any true alignment information between the speech and the image. “The biggest contribution of the paper,” Harwath says, “is demonstrating that these cross-modal [audio and visual] alignments can be inferred automatically by simply teaching the network which images and captions belong together and which pairs don’t.”</p>
<p>The authors dub this automatic-learning association between a spoken caption’s waveform with the image pixels a “matchmap.” After training on thousands of image-caption pairs, the network narrows down those alignments to specific words representing specific objects in that matchmap.</p>
<p>“It’s kind of like the Big Bang, where matter was really dispersed, but then coalesced into planets and stars,” Harwath says. “Predictions start dispersed everywhere but, as you go through training, they converge into an alignment that represents meaningful semantic groundings between spoken words and visual objects.”</p>
<p>“It is exciting to see that neural methods are now also able to associate image elements with audio segments, without requiring text as an intermediary,” says Florian Metze, an associate research professor at the Language Technologies Institute at Carnegie Mellon University. “This is not human-like learning; it’s based entirely on correlations, without any feedback, but it might help us understand how shared representations might be formed from audio and visual cues. ... [M]achine [language] translation is an application, but it could also be used in documentation of endangered languages (if the data requirements can be brought down). One could also think about speech recognition for non-mainstream use cases, such as people with disabilities and children.”</p>
MIT computer scientists have developed a system that learns to identify objects within an image, based on a spoken description of the image.Image: Christine DaniloffResearch, Computer science and technology, Language, Machine learning, Artificial intelligence, Data, Algorithms, Computer vision, Electrical engineering and computer science (EECS), Computer Science and Artificial Intelligence Laboratory (CSAIL), School of EngineeringHelping computers fill in the gaps between video frames https://news.mit.edu/2018/machine-learning-video-activity-recognition-0914
Machine learning system efficiently recognizes activities by observing how objects change in only a few key frames.Thu, 13 Sep 2018 23:59:59 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/machine-learning-video-activity-recognition-0914<p>Given only a few frames of a video, humans can usually surmise what is happening and will happen on screen. If we see an early frame of stacked cans, a middle frame with a finger at the stack’s base, and a late frame showing the cans toppled over, we can guess that the finger knocked down the cans. Computers, however, struggle with this concept.</p>
<p>In a paper being presented at this week’s European Conference on Computer Vision, MIT researchers describe an add-on module that helps artificial intelligence systems called convolutional neural networks, or CNNs, to fill in the gaps between video frames to greatly improve the network’s activity recognition.</p>
<p>The researchers’ module, called Temporal Relation Network (TRN), learns how objects change in a video at different times. It does so by analyzing a few key frames depicting an activity at different stages of the video — such as stacked objects that are then knocked down. Using the same process, it can then recognize the same type of activity in a new video.</p>
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<p>In experiments, the module outperformed existing models by a large margin in recognizing hundreds of basic activities, such as poking objects to make them fall, tossing something in the air, and giving a thumbs-up. It also more accurately predicted what will happen next in a video — showing, for example, two hands making a small tear in a sheet of paper —&nbsp;given only a small number of early frames.</p>
<p>One day, the module could be used to help robots better understand what’s going on around them.</p>
<p>“We built an artificial intelligence system to recognize the transformation of objects, rather than appearance of objects,” says Bolei Zhou, a former PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) who is now an assistant professor of computer science at the Chinese University of Hong Kong. “The system doesn’t go through all the frames — it picks up key frames and, using the temporal relation of frames, recognize what’s going on. That improves the efficiency of the system and makes it run in real-time accurately.”</p>
<p>Co-authors on the paper are CSAIL principal investigator Antonio Torralba, who is also a professor in the Department of Electrical Engineering and Computer Science; CSAIL Principal Research Scientist Aude Oliva; and CSAIL Research Assistant Alex Andonian.</p>
<p><strong>Picking up key frames</strong></p>
<p>Two common CNN modules being used for activity recognition today suffer from efficiency and accuracy drawbacks. One model is accurate but must analyze each video frame before making a prediction, which is computationally expensive and slow. The other type, called two-stream network, is less accurate but more efficient. It uses one stream to extract features of one video frame, and then merges the results with “optical flows,” a stream of extracted information about the movement of each pixel. Optical flows are also computationally expensive to extract, so the model still isn’t that efficient.</p>
<p>“We wanted something that works in between those two models — getting efficiency and accuracy,” Zhou says.</p>
<p>The researchers trained and tested their module on three crowdsourced datasets of short videos of various performed activities. The first dataset, called Something-Something, built by the company TwentyBN, has more than 200,000 videos in 174 action categories, such as poking an object so it falls over or lifting an object. The second dataset, Jester, contains nearly 150,000 videos with 27 different hand gestures, such as giving a thumbs-up or swiping left. The third, Charades, built by Carnegie Mellon University researchers, has nearly 10,000 videos of 157 categorized activities, such as carrying a bike or playing basketball.</p>
<p>When given a video file, the researchers’ module simultaneously processes ordered frames — in groups of two, three, and four — spaced some time apart. Then it quickly assigns a probability that the object’s transformation across those frames matches a specific activity class. For instance, if it processes two frames, where the later frame shows an object at the bottom of the screen and the earlier shows the object at the top, it will assign a high probability to the activity class, “moving object down.” If a third frame shows the object in the middle of the screen, that probability increases even more, and so on. From this, it learns object-transformation features in frames that most represent a certain class of activity.</p>
<p><strong>Recognizing and forecasting activities</strong></p>
<p>In testing, a CNN equipped with the new module accurately recognized many activities using two frames, but the accuracy increased by sampling more frames. For Jester, the module achieved top accuracy of 95 percent in activity recognition, beating out several existing models. &nbsp;</p>
<p>It even guessed right on ambiguous classifications: Something-Something, for instance, included actions such as “pretending to open a book” versus “opening a book.” To discern between the two, the module just sampled a few more key frames, which revealed, for instance, a hand near a book in an early frame, then on the book, then moved away from the book in a later frame.</p>
<p>Some other activity-recognition models also process key frames but don’t consider temporal relationships in frames, which reduces their accuracy. The researchers report that their TRN module nearly doubles in accuracy over those key-frame models in certain tests.</p>
<p>The module also outperformed models on forecasting an activity, given limited frames. After processing the first 25 percent of frames, the module achieved accuracy several percentage points higher than a baseline model. With 50 percent of the frames, it achieved 10 to 40 percent higher accuracy. Examples include determining that a paper would be torn just a little, based how two hands are positioned on the paper in early frames, and predicting that a raised hand, shown facing forward, would swipe down.</p>
<p>“That’s important for robotics applications,” Zhou says. “You want [a robot] to anticipate and forecast what will happen early on, when you do a specific action.”</p>
<p>“In this article the authors suggest a simple yet powerful technique to model temporal dependencies across different time scales by just observing a small number of frames from each video,” says Dan Gutfreund, a researcher at the IBM-MIT Laboratory for Brain-inspired Multi-Media Machine Comprehension, who has worked with the researchers before but was not involved in this study. “The resulting model does not only provide state-of-the-art accuracy on several action recognition benchmark datasets, but also, it is significantly more efficient than previously suggested models. This makes this model an excellent candidate for various applications, for example in robotics, accessibility for blind people by providing visual information in real time, self-driving cars, security and more.”&nbsp;</p>
<p>Next, the researchers aim to improve the module’s sophistication. The first step is implementing object recognition together with activity recognition. Then, they hope to add in “intuitive physics,” meaning helping it understand real-world physical properties of objects. “Because we know a lot of the physics inside these videos, we can train module to learn such physics laws and use those in recognizing new videos,” Zhou says. “We also open source all the code and models. Activity understanding is an exciting area of artificial intelligence right now.”</p>
MIT researchers have developed a module that helps artificial-intelligence systems fill in the gaps between video frames to improve activity recognition.Image courtesy of the researchers, edited by MIT NewsResearch, Computer science and technology, Computer vision, Algorithms, Robotics, Robots, Video, Artificial intelligence, School of Engineering, Electrical Engineering & Computer Science (eecs), Computer Science and Artificial Intelligence Laboratory (CSAIL)Artificial intelligence system uses transparent, human-like reasoning to solve problemshttps://news.mit.edu/2018/mit-lincoln-laboratory-ai-system-solves-problems-through-human-reasoning-0911
Model from MIT Lincoln Laboratory Intelligence and Decision Technologies Group sets a new standard for understanding how a neural network makes decisions.Tue, 11 Sep 2018 10:20:00 -0400Kylie Foy | Lincoln Laboratoryhttps://news.mit.edu/2018/mit-lincoln-laboratory-ai-system-solves-problems-through-human-reasoning-0911<p>A child is presented with a picture of various shapes and is asked to find the big red circle. To come to the answer, she goes through a few steps of reasoning: First, find all the big things; next, find the big things that are red; and finally, pick out the big red thing that’s a circle.</p>
<p>We learn through reason how to interpret the world. So, too, do neural networks. Now a&nbsp;team of researchers from MIT Lincoln Laboratory's Intelligence and Decision Technologies Group has developed a neural network that performs human-like reasoning steps to answer questions about the contents of images. Named the Transparency by Design Network (TbD-net), the model visually renders its thought process&nbsp;as it solves problems, allowing human analysts to interpret its decision-making process. The model performs better than today’s best visual-reasoning neural networks. &nbsp;</p>
<p>Understanding how a neural network comes to its decisions has been a long-standing challenge for artificial intelligence (AI) researchers. As the neural&nbsp;part of their name suggests, neural networks are brain-inspired AI systems intended to replicate the way that humans learn. They consist of input and output layers, and layers in between that transform the input into the correct output. Some deep neural networks have grown so complex that it’s practically impossible to follow this transformation process. That's&nbsp;why they are referred to as "black box” systems, with their&nbsp;exact goings-on inside&nbsp;opaque even to the engineers who build them.</p>
<p>With TbD-net, the developers aim to make these inner workings transparent. Transparency is important because it allows humans to interpret an AI's results.</p>
<p>It is important to&nbsp;know, for example, what exactly a neural network used in self-driving cars thinks the difference is between a pedestrian and stop sign, and at what point&nbsp;along its chain of reasoning does it see&nbsp;that&nbsp;difference. These insights allow researchers to&nbsp;teach the neural network to correct any incorrect&nbsp;assumptions. But the TbD-net developers say the best neural networks today lack an effective mechanism for enabling humans to understand their reasoning process.</p>
<p>"Progress on improving performance in visual reasoning has come at the cost of interpretability,” says&nbsp;Ryan Soklaski, who built TbD-net with fellow researchers Arjun Majumdar, David Mascharka, and Philip Tran.</p>
<p>The Lincoln Laboratory group was able to close the gap between performance and interpretability with TbD-net. One key to their system is a collection of "modules," small neural networks that are specialized to perform specific subtasks. When TbD-net is asked a visual reasoning question about an image, it breaks down the question into subtasks and assigns the appropriate module to fulfill its part. Like workers down an assembly line, each module builds off what the module before it has figured out to eventually produce the final, correct answer.&nbsp;As a whole, TbD-net utilizes one AI technique that interprets human language questions and breaks those sentences into subtasks, followed by multiple computer vision AI techniques that interpret the imagery.</p>
<p>Majumdar says: "Breaking a complex chain of reasoning into a series of smaller subproblems, each of which can be solved independently and composed, is a powerful and intuitive means for reasoning."</p>
<p>Each module's output is depicted visually in what the group calls&nbsp;an "attention mask." The&nbsp;attention mask shows heat-map blobs over objects in the image that the module is identifying as its answer. These visualizations let the human analyst see how a module is interpreting the image.&nbsp; &nbsp;</p>
<p>Take, for example, the following question posed to TbD-net: “In this image, what color is the large metal cube?" To answer the question, the first module locates large objects only, producing an attention mask with those large objects highlighted. The next module takes this output and finds which of those objects identified as large by the previous module are also metal. That module's output is sent to the next module, which identifies which of those large, metal objects is also a cube. At last, this output is sent to a module that can determine the color of objects. TbD-net’s final output is “red,” the correct answer to the question.&nbsp;</p>
<p>When tested, TbD-net achieved results that surpass the best-performing visual reasoning models. The researchers evaluated the model using a visual question-answering dataset consisting of 70,000 training images and 700,000 questions, along with test and validation sets of 15,000 images and 150,000 questions. The initial model achieved 98.7 percent test accuracy on the dataset, which, according to the researchers, far outperforms other neural module network–based approaches.</p>
<p>Importantly, the researchers were able to then improve these results because of their model's key advantage — transparency. By looking at the attention masks produced by the modules, they could see where things went wrong and refine the model. The end result was a state-of-the-art performance of 99.1 percent accuracy.</p>
<p>"Our model provides straightforward, interpretable outputs at every stage of the visual reasoning process,” Mascharka says.</p>
<p>Interpretability is especially valuable if deep learning algorithms are to be deployed alongside humans to help tackle complex real-world tasks. To build trust in these systems, users will need the ability to inspect the reasoning process so that they can understand why and how a model could make wrong predictions.&nbsp;</p>
<p>Paul Metzger, leader of the Intelligence and Decision Technologies Group, says the research&nbsp;“is part of Lincoln Laboratory’s work toward becoming a world leader in applied machine learning research and artificial intelligence that fosters human-machine collaboration.”</p>
<p>The details of this work are described in the paper, “<a href="https://arxiv.org/abs/1803.05268" target="_blank">Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning</a>," which was presented at the Conference on Computer Vision and Pattern Recognition (CVPR) this summer.</p>
TbD-net solves the visual reasoning problem by breaking it down to a chain of subtasks. The answer to each subtask is shown in heat maps highlighting the objects of interest, allowing analysts to see the network's thought process.Illustration courtesy of the Intelligence and Decision Technologies GroupLincoln Laboratory, Artificial intelligence, Machine learning, Algorithms, Computer science and technology, ResearchSmoothing out sketches’ rough edgeshttps://news.mit.edu/2018/automated-vectorization-algorithm-animation-0911
MIT-developed tool improves automated image vectorization, saving digital artists time and effort.Tue, 11 Sep 2018 00:00:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/automated-vectorization-algorithm-animation-0911<p>Artists may soon have at their disposal a new MIT-developed tool that could help them create digital characters, logos, and other graphics more quickly and easily.&nbsp;</p>
<p>Many digital artists rely on image vectorization, a technique that converts a pixel-based image into an image comprising groupings of clearly defined shapes. In this technique, points in the image are connected by lines or curves to construct the shapes. Among other perks, vectorized images maintain the same resolution when either enlarged or shrunk down.</p>
<p>To vectorize an image, artists often have to hand-trace each stroke using specialized software, such as Adobe Illustrator, which is laborious. Another option is using automated vectorization tools in those software packages. Often, however, these tools lead to numerous tracing errors that take more time to rectify by hand. The main culprit: mismatches at intersections where curves and lines meet.</p>
<p>In <a href="http://arxiv.org/pdf/1801.01922.pdf" target="_blank">a paper</a> being published in the journal <em>ACM Transactions on Graphics</em>, MIT researchers detail a new automated vectorization algorithm that traces intersections without error, greatly reducing the need for manual revision. Powering the tool is a modified version of a new mathematical technique in the computer-graphics community, called “frame fields,” used to guide tracing of paths around curves, sharp corners, and messy parts of drawings where many lines intersect.</p>
<p>The tool could save digital artists significant time and frustration. “A rough estimate is that it could save 20 to 30 minutes from automated tools, which is substantial when you think about animators who work with multiple sketches,” says first author Mikhail Bessmeltsev, a former Computer Science and Artificial Intelligence Laboratory (CSAIL) postdoc associate who is now an assistant professor at the University of Montreal. “The hope is to make automated vectorization tools more practical for artists who care about the quality of their work.”</p>
<p>Co-author on the paper is Justin Solomon, an assistant professor in CSAIL and in the Department of Electrical Engineering and Computer Science, and a principal investigator in the Geometric Data Processing Group.</p>
<p><strong>Guiding the lines</strong></p>
<p>Many modern tools used to model 3-D shapes directly from artist sketches, including Bessmeltsev’s previous research projects, require vectorizing the drawings first. Automated vectorization “never worked for me, so I got frustrated,” he says. Those tools, he says, are fine for rough alignments but aren’t designed for precision: “Imagine you’re an animator and you drew a couple frames of animation. They’re pretty clean sketches, and you want to edit or color them on a computer. For that, you really care how well your vectorization aligns with your pencil drawing.”</p>
<p>Many errors, he noted, come from misalignment between the original and vectorized image at junctions where two curves meet — in a type of “X” junction — and where one line ends at another — in a “T” junction. Previous research and software used models incapable of aligning the curves at those junctions, so Bessmeltsev and Solomon took on the task.</p>
<p>The key innovation came from using frame fields to guide tracing. Frame fields assign two directions to each point of a 2-D or 3-D shape. These directions overlay a basic structure, or topology, that can guide geometric tasks in computer graphics. Frame fields have been used, for instance, to restore destroyed historical documents and to convert triangle meshes — networks of triangles covering a 3-D shape — into quadrangle meshes — grids of four-sided shapes. Quad meshes are commonly used to create computer-generated characters in movies and video games, and for computer-aided design (CAD) for better real-world design and simulation.</p>
<p>Bessmeltsev, for the first time, applied frame fields to image vectorization. His frame fields assign two directions to every dark pixel on an image. This keeps track of the tangent directions — where a curve meets a line — of nearby drawn curves. That means, at every intersection of a drawing, the two directions of the frame field align with the directions of the intersecting curves. This drastically reduces the roughness, or noise, surrounding intersections, which usually makes them difficult to trace.</p>
<p>“At a junction, all you have to do is follow one direction of the frame field and you get a smooth curve. You do that for every junction, and all junctions will then be aligned properly,” Bessmeltsev says.</p>
<p><strong>Cleaner vectorization</strong></p>
<p>When given an input of a pixeled raster 2-D drawing with one color per pixel, the tool assigns each dark pixel a cross that indicates two directions. Starting at some pixel, it first chooses a direction to trace. Then, it traces the vector path along the pixels, following the directions. After tracing, the tool creates a graph capturing connections between the solid strokes in the drawn image. Using this graph, the tool matches the necessary lines and curves to those strokes and automatically vectorizes the image.</p>
<p>In their paper, the researchers demonstrated their tool on various sketches, such as cartoon animals, people, and plants. The tool cleanly vectorized all intersections that were traced incorrectly using traditional tools. With traditional tools, for instance, lines around facial features, such as eyes and teeth, didn’t stop where the original lines did or ran through other lines.</p>
<p>One example in the paper shows pixels making up two slightly curved lines leading to the tip of a hat worn by a cartoon elephant. There’s a sharp corner where the two lines meet. Each dark pixel contains a cross that’s straight or slightly slanted, depending on the curvature of the line. Using those cross directions, the traced line could easily follow as it swooped around the sharp turn.</p>
<p>“Many artists still enjoy and prefer to work with real media (for example, pen, pencil, and paper). … The problem is that the scanning of such content into the computer often results in a severe loss of information,” says Nathan Carr, a principal researcher in computer graphics at Adobe Systems Inc., who was not involved in the research. “[The MIT] work relies on a mathematical construct known as ‘frame fields,’ to clean up and disambiguate scanned sketches to gain back this loss of information.&nbsp;It’s a great application of using mathematics to facilitate the artistic workflow in a clean well-formed manner. In summary, this work is important, as it aids in the ability for artists to transition between the physical and digital realms.”</p>
<p>Next, the researchers plan to augment the tool with a temporal-coherence technique, which extracts key information from adjacent animation frames. The idea would be to vectorize the frames simultaneously, using information from one to adjust the line tracing on the next, and vice versa. “Knowing the sketches don’t change much between the frames, the tool could improve the vectorization by looking at both at the same time,” Bessmeltsev says.</p>
MIT researchers have developed an algorithm that traces intersections in sketches without error. This could save digital artists significant time and frustration when vectorizing an image for animation, marketing logos, and other applications.Image: Ivan HuskaResearch, Computer science and technology, Art, Algorithms, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of EngineeringRobots can now pick up any object after inspecting ithttps://news.mit.edu/2018/mit-csail-robots-can-pick-any-object-after-inspection-0910
Breakthrough CSAIL system suggests robots could one day be able to see well enough to be useful in people’s homes and offices.Mon, 10 Sep 2018 00:00:00 -0400Adam Conner-Simons | Rachel Gordon | CSAILhttps://news.mit.edu/2018/mit-csail-robots-can-pick-any-object-after-inspection-0910<p>Humans have long been masters of dexterity, a skill that can largely be credited to the help of our eyes. Robots, meanwhile, are still catching up.</p>
<p>Certainly there’s been some progress: For decades, robots in controlled environments like assembly lines have been able to pick up the same object over and over again. More recently, breakthroughs in computer vision have enabled robots to make basic distinctions between objects. Even then, though, the systems don’t truly understand objects’ shapes, so there’s little the robots can do after a quick pick-up. &nbsp;</p>
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<p>In a new paper, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), say that they’ve made a key development in this area of work: a system that lets robots inspect random objects, and visually understand them enough to accomplish specific tasks without ever having seen them before.</p>
<p>The system, called&nbsp;Dense Object Nets&nbsp;(DON), looks at objects as collections of points that serve as sort of visual roadmaps. This approach lets robots better understand and manipulate items, and, most importantly, allows them to even pick up a specific object among a clutter of similar —&nbsp;a valuable skill for the kinds of machines that companies like Amazon and Walmart use in their warehouses.</p>
<p>For example, someone might use DON to get a robot to grab onto a specific spot on an object,&nbsp;say, the tongue of a shoe. From that, it can look at a shoe it has never seen before, and successfully grab its tongue.</p>
<p>"Many approaches to manipulation can’t identify specific parts of an object across the many orientations that object may encounter,” says PhD student Lucas Manuelli, who wrote a new paper about the system with lead author and fellow PhD student Pete Florence, alongside MIT Professor Russ Tedrake. “For example, existing algorithms would be unable to grasp a mug by its handle, especially if the mug could be in multiple orientations, like upright, or on its side."</p>
<p>The team views potential applications not just in manufacturing settings, but also in homes. Imagine giving the system an image of a tidy house, and letting it clean while you’re at work, or using an image of dishes so that the system puts your plates away while you’re on vacation.</p>
<p>What’s also noteworthy is that none of the data was actually labeled by humans. Instead, the system is what the team calls “self-supervised,” not requiring&nbsp;any human annotations.</p>
<p>Two common approaches to robot grasping involve either task-specific learning, or creating a general grasping algorithm. These techniques both have obstacles: Task-specific methods are difficult to generalize to other tasks, and general grasping doesn’t get specific enough to deal with the nuances of particular tasks, like putting objects in specific spots.</p>
<p>The DON system, however, essentially creates a series of coordinates on a given object, which serve as a kind of visual roadmap, to give the robot a better understanding of what it needs to grasp, and where.</p>
<p>The team trained the system to look at objects as a series of points that make up a larger coordinate system. It can then map different points together to visualize an object’s 3-D shape, similar to how panoramic photos are stitched together from multiple photos. After training, if a person specifies a point on a object, the robot can take a photo of that object, and identify and match points to be able to then pick up the object at that specified point.</p>
<p>This is different from systems like UC-Berkeley’s DexNet, which can grasp many different items, but can’t satisfy a specific request. Imagine a child&nbsp;at 18 months old, who doesn't understand which toy you want it to play with but can still grab lots of items, versus a four-year old who can respond to "go grab your truck by the red end of it.”</p>
<p>In one set of tests done on a soft caterpillar toy, a Kuka robotic arm powered by DON could grasp the toy’s right ear from a range of different configurations. This showed that, among other things, the system has the ability to distinguish left from right on symmetrical objects.</p>
<p>When testing on a bin of different baseball hats, DON could pick out a specific target hat despite all of the hats having very similar designs —&nbsp;and having never seen pictures of the hats in training data before.</p>
<p>“In factories robots often need complex part feeders to work reliably,” says Florence. “But a system like this that can understand objects’ orientations could just take a picture and be able to grasp and adjust the object accordingly.”</p>
<p>In the future, the team hopes to improve the system to a place where it can perform specific tasks with a deeper understanding of the corresponding objects, like learning how to grasp an object and move it with the ultimate goal of say, cleaning a desk.</p>
<p>The team will present their paper on the system next month at the Conference on Robot Learning in Zürich, Switzerland.</p>
PhD student Lucas Manuelli worked with lead author Pete Florence to develop a system that uses advanced computer vision to enable a Kuka robot to pick up virtually any object.Photo: Tom Buehler/CSAILRobots, Robotics, School of Engineering, Electrical Engineering & Computer Science (eecs), Computer Science and Artificial Intelligence Laboratory (CSAIL), Research, Computer science and technology, Computer visionMost popular MITx MOOC reaches 1.2 million enrollmentshttps://news.mit.edu/2018/first-mitx-mooc-reaches-enrollment-milestone-0830
Since its first online offering in 2012, Introduction to Computer Science using Python from MITx has become the most popular MOOC in MIT history.Thu, 30 Aug 2018 12:50:01 -0400Alice McCarthy | MIT Open Learninghttps://news.mit.edu/2018/first-mitx-mooc-reaches-enrollment-milestone-0830<p>Since it was conceived as an online offering in 2012, the <em>MITx</em> massive open online course (MOOC), <a href="https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-11?utm_medium=partner-marketing&amp;utm_source=organic&amp;utm_campaign=mit-news&amp;utm_content=6.001-mit-news-article">Introduction to Computer Science using Python</a>, has become the most popular MOOC in MIT history with 1.2 million enrollments to date.</p>
<p>The course is derived from a campus-based and <a href="https://ocw.mit.edu/index.htm" target="_blank">Open CourseWare</a> subject at MIT developed and originally taught at MIT by John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering. “Although on the surface it’s a computer programming course with Python, it’s really not about Python or even programming,” explains Guttag. “It’s about teaching students to use computation, in this case described by Python, to build models and explore broader questions of what can be done with computation to understand the world.”</p>
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<p>The first <em>MITx</em> version of this course, launched in 2012, was co-developed by Guttag and Eric Grimson, the Bernard M. Gordon Professor of Medical Engineering and professor of computer science. It was one of the very first MOOCs offered by MIT on the <a href="https://www.edx.org/" target="_blank">edX platform</a>.</p>
<p>“This course is designed to help students begin to think like a computer scientist,” says Grimson. “By the end of it, the student should feel very confident that given a problem, whether it’s something from work or their personal life, they could use computation to solve that problem.”</p>
<p>The course was initially developed as a 13-week course, but in 2014 it was separated into two courses, 6.00.1x and 6.00.2x. “We achieved 1.4 million enrollments at the beginning of the summer with both courses combined,” says Ana Bell, lecturer in electrical engineering and computer science, who keeps the MOOC current by adding new problem sets, adding exercises, and coordinating staff volunteer teaching assistants (TAs). “At its core, the 6.00 series teaches computational thinking,” adds Bell. “It does this using the Python programming language, but the course also teaches programming concepts that can be applied in any other programming language.”&nbsp;</p>
<p>Enrollment is already high for the <a href="https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-11?utm_medium=partner-marketing&amp;utm_source=organic&amp;utm_campaign=mit-news&amp;utm_content=6.001-mit-news-article" target="_blank">next 6.001x course</a>, which starts today. Guttag, Grimson, and Bell suggest several reasons for the course’s popularity. For example, many learners are older or are switching careers and either have not been exposed to computer science much or are looking for new skills. “Many learners take it because they see computer science as a path forward and something they need to know,” says Grimson.&nbsp;</p>
<p><strong>Providing new lives for refugees </strong></p>
<p>Such is the case of Muhammad Enjari, a 39-year-old petroleum engineer from Homs, Syria. He fled Homs with his wife and 3 children at the beginning of the Syrian revolution and settled in Jordan soon after. “I have a degree in petroleum engineering but in Jordan I could not find a job,” he says.</p>
<p>In his journey to jumpstart a new career, Enjari enrolled in 6.00.1x as part of the <a href="http://react.mit.edu/">MIT Refugee Action Hub</a>, or ReACT, a yearlong Computer and Data Science Program (CDSP) curriculum. He received a 100 percent on the final exam and a 100 percent final grade. “Because of this course and others, I will be starting a new job in two weeks as a paid intern in computer engineering with Edraak, a MOOC platform similar to edX for Arabic-speaking students,” he adds.</p>
<p>Similarly, when 23-year-old Manda Awad, another ReACT CDSP student, enrolled in the 6.00.1x course as a refugee from Palestine living in Jordan, she learned that some of the material/topics covered in the course series was not included in her computer science curriculum at the University of Jordan. This, coupled with a lack of support for women in tech, inspired Awad to write a proposal that would update the engineering department’s computer science curricula by integrating the 6.00 series coursework, and expand access to the material across the student body. “I want to take what I have learned and teach other students, particularly women,” she says. Awad is currently setting up a programming club with a weekly&nbsp;instructional&nbsp;segment. She has a goal of introducing a “Women who Code”&nbsp;group to the Zaatari refugee camp in Jordan, which she plans to launch in the next year.</p>
<p><strong>Expanding career options </strong></p>
<p>Grain farmer Matt Reimer of Manitoba, Canada, enrolled in the course to develop a computer program to improve his farm’s efficiency, productivity, and profitability. He gained the skills needed to use remote-control technology to accelerate harvest production using his farm’s auto-steering tractor integrated with his grain combine harvester. The result: The driverless tractor unloaded grain from the combine over 500 times, saving the farm an estimated $5,000 or more.</p>
<p>When Ruchi Garg decided to re-enter the workplace after being the primary caretaker for her two young children, she enrolled in the course to get her former technology career moving again. She was worried that her skillset had grown stale in the wake of rapidly advancing technologies and evolving computer engineering practices. After completing 6.00.1x, Garg has gone on to become a data analyst at The Weather Company, an IBM subsidiary.</p>
<p>Aditi, a blind data security professional based in India, enrolled in 6.00.1x to help create the next generation of security tools. The MIT course was the first completely accessible course she had ever taken online. After finishing the 6.00 series, Aditi will be attending Georgia Tech in the fall for her master’s degree.</p>
<p>And in 2017, <em>MITx</em> partnered with Silicon Valley-based San Jose City College to offer the course as part of a program for students in the area who traditionally have not had access to computer science curriculum. When students complete the course, they are matched with prospective employers for internships and possible employment in the area’s technology industry.</p>
<p><strong>Past students stay involved</strong></p>
<p>Because of her own enthusiasm with the course, Estefania Cassingena Navone became a Community TA for <em>MITx</em> from Venezuela. She has written several supporting documents with visualizations to demystify some of the more complex ideas in the course. “This course gave me the hope I needed,” she says. “Hope that living in a developing country would not be a barrier to achieve what I truly want to achieve in life, it gave me the opportunity to be part of an online community where hard work and dedication really helps you thrive.”&nbsp;</p>
<p>After taking the course, <em>MITx</em> TA Thomas Ballatore felt empowered to learn more about using computers for his own teaching. Although he has already earned a PhD, he has entered a master’s program majoring in digital media design learning how to produce his own online courses. “I became a TA because of my love of teaching and knew that the best way to truly learn material is to explain it to others.” Now on his fourth cycle of assisting, he has created several tutorial videos, motivated by helping others get their ‘ah-hah’ moments as well.</p>
<p>“This course essentially embodies the MIT spirit of drinking from the firehose,” says Ana Bell. “It's a tough course and fast-paced. If you get through it, you are rewarded with an immense feeling of accomplishment.” And perhaps, also, a new life-changing opportunity.</p>
Learners from around the world have completed the online course Introduction to Computer Science using Python through MITx on edX. Massive open online courses (MOOCs), MITx, EdX, STEM education, online learning, Classes and programs, Computer science and technology, IDSS, Office of Open Learning, Women in STEM, Global, School of Engineering, education, teaching, Education, teaching, academicsModel improves prediction of mortality risk in ICU patientshttps://news.mit.edu/2018/model-improves-prediction-mortality-risk-icu-patients-0829
By training on patients grouped by health status, neural network can better estimate if patients will die in the hospital.Wed, 29 Aug 2018 12:30:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/model-improves-prediction-mortality-risk-icu-patients-0829<p>In intensive care units, where patients come in with a wide range of health conditions, triaging relies heavily on clinical judgment. ICU staff run numerous physiological tests, such as bloodwork and checking vital signs, to determine if patients are at immediate risk of dying if not treated aggressively.</p>
<p>Enter: machine learning. Numerous models have been developed in recent years to help predict patient mortality in the ICU, based on various health factors during their stay. These models, however, have performance drawbacks. One common type of “global” model is trained on a single large patient population. These might work well on average, but poorly on some patient subpopulations. On the other hand, another type of model analyzes different subpopulations —&nbsp;for instance, those grouped by similar conditions, patient ages, or hospital departments — but often have limited data for training and testing.</p>
<p>In a paper recently presented at the Proceedings of Knowledge Discovery and Data Mining conference, MIT researchers describe a machine-learning model that functions as the best of both worlds: It trains specifically on patient subpopulations, but also shares data across all subpopulations to get better predictions. In doing so, the model can better predict a patient’s risk of mortality during their first two days in the ICU, compared to strictly global and other models.</p>
<p>The model first crunches physiological data in electronic health records of previously admitted ICU patients, some who had died during their stay. In doing so, it learns high predictors of mortality, such as low heart rate, high blood pressure, and various lab test results — high glucose levels and white blood cell count, among others — over the first few days and breaks the patients into subpopulations based on their health status. Given a new patient, the model can look at that patient’s physiological data from the first 24 hours and, using what it’s learned through analyzing those patient subpopulations, better estimate the likelihood that the new patient will also die in the following 48 hours.</p>
<p>Moreover, the researchers found that evaluating (testing and validating) the model by specific subpopulations also highlights performance disparities of global models in predicting mortality across patient subpopulations. This is important information for developing models that can more accurately work with specific patients.</p>
<p>“ICUs are very high-bandwidth, with a lot of patients,” says first author Harini Suresh, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “It’s important to figure out well ahead of time which patients are actually at risk and in more need of immediate attention.”</p>
<p>Co-authors on the paper are CSAIL graduate student Jen Gong, and John Guttag, the Dugald C. Jackson Professor in Electrical Engineering.</p>
<p><strong>Multitasking and patient subpopulations</strong></p>
<p>A key innovation of the work is that, during training, the model separates patients into distinct subpopulations, which captures aspects of a patient’s overall state of health and mortality risks. It does so by calculating a combination of physiological data, broken down by the hour. Physiological data include, for example, levels of glucose, potassium, and nitrogen, as well as heart rate, blood pH, oxygen saturation, and respiratory rate. Increases in blood pressure and potassium levels — a sign of a heart failure — may indicate health problems over other subpopulations.</p>
<p>Next, the model employs a multitasking method of learning to build predictive models. When the patients are broken into subpopulations, differently tuned models are assigned to each subpopulation. Each variant model can then more accurately make predictions for its personalized group of patients. This approach also allows the model to share data across all subpopulations when it’s making predictions. When given a new patient, it will match the patient’s physiological data to all subpopulations, find the best fit, and then better estimate the mortality risk from there.</p>
<p>“We’re using all the patient data and sharing information across populations where it’s relevant,” Suresh says. “In this way, we’re able to … not suffer from data scarcity problems, while taking into account the differences between the different patient subpopulations.”</p>
<p>“Patients admitted to the ICU often differ in why they’re there and what their health status is like. Because of this, they’ll be treated very differently,” Gong adds. Clinical decision-making aids “should account for the heterogeneity of these patient populations … and make sure there is enough data for accurate predictions.”</p>
<p>A key insight from this method, Gong says, came from using a multitasking approach to also evaluate a model’s performance on specific subpopulations. Global models are often evaluated in overall performance, across entire patient populations. But the researchers’ experiments showed these models actually underperform on subpopulations. The global model tested in the paper predicted mortality fairly accurately overall, but dropped several percentage points in accuracy when tested on individual subpopulations.</p>
<p>Such performance disparities are difficult to measure without evaluating by subpopulations, Gong says: “We want to evaluate how well our model does, not just on a whole cohort of patients, but also when we break it down for each cohort with different medical characteristics. That can help researchers in better predictive model training and evaluation.”</p>
<p><strong>Getting results</strong></p>
<p>The researchers tested their model using data from the MIMIC Critical Care Database, which contains scores of data on heterogeneous patient populations. Of around 32,000 patients in the dataset, more than 2,200 died in the hospital. They used 80 percent of the dataset to train, and 20 percent to test the model.</p>
<p>In using data from the first 24 hours, the model clustered the patients into subpopulations with important clinical differences. Two subpopulations, for instance, contained patients with elevated blood pressure over the first several hours — but one decreased over time, while the other maintained the elevation throughout the day. This subpopulation had the highest mortality rate.</p>
<p>Using those subpopulations, the model predicted the mortality of the patients over the following 48 hours with high specificity and sensitivity, and various other metrics. The multitasking model significantly outperformed a global model by several percentage points.</p>
<p>Next, the researchers aim to use more data from electronic health records, such as treatments the patients are receiving. They also hope, in the future, to train the model to extract keywords from digitized clinical notes and other information.</p>
<p>The work was supported by the National Institutes of Health.</p>
MIT researchers have developed a machine-learning model that groups patients into subpopulations by health status to better predict a patient’s risk of dying during their stay in the ICU. This technique outperforms "global" mortality-prediction models and reveals performance disparities of those models across specific patient subpopulations. Research, Health care, Computer science and technology, Machine learning, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering, National Institutes of Health (NIH)MIT-created programming language Julia 1.0 debutshttps://news.mit.edu/2018/mit-developed-julia-programming-language-debuts-juliacon-0827
The dynamic programming language, which is free and open source, combines the speed and popular features of the best scientific and technical software.Mon, 27 Aug 2018 14:40:00 -0400Sandi Miller | Department of Mathematicshttps://news.mit.edu/2018/mit-developed-julia-programming-language-debuts-juliacon-0827<p>After years of tinkering, the dynamic programming language Julia 1.0 was officially&nbsp;<a href="https://julialang.org/blog/2018/08/one-point-zero">released</a>&nbsp;to the public during<a href="http://juliacon.org/2018/"> JuliaCon</a>, an annual conference of Julia users held recently in London.</p>
<p>The release of Julia 1.0 is a huge Julia milestone since MIT Professor Alan Edelman, Jeff Bezanson, Stefan Karpinski, and Viral Shah <a href="http://julialang.org/blog/2012/02/why-we-created-julia">released&nbsp;Julia to developers</a>&nbsp;in 2012, says Edelman.</p>
<p>&nbsp;“Julia has been revolutionizing scientific and technical computing since 2009,” says&nbsp;Edelman, the year&nbsp;the creators started working on a new language that combined the best features of Ruby, MatLab, C, Python, R, and others. Edelman is&nbsp;director of the<a href="https://julia.mit.edu/">&nbsp;Julia Lab at MIT</a>&nbsp;and one of the co-creators of the language at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL).&nbsp;</p>
<p>Julia, which was developed and incubated at MIT, is free and open source, with more than 700 active open source contributors, 1,900 registered packages, 41,000&nbsp;GitHub stars, 2 million downloads, and a reported 101 percent&nbsp;annual rate of download growth. It is used at more than 700 universities and research institutions and by companies such as Aviva, BlackRock, Capital One, and Netflix.</p>
<p>At MIT, Julia users and developers include professors Steven Johnson, Juan Pablo Vielma, Gilbert Strang, Robin Deits, Twan Koolen, and Robert Moss. Julia is also used by MIT Lincoln Laboratory and the Federal Aviation Administration to develop the<a href="https://juliacomputing.com/case-studies/lincoln-labs.html"> Next-Generation Airborne Collision Avoidance System (ACAS-X)</a>, by the MIT Operations Research Center to <a href="http://online.wsj.com/public/resources/documents/print/WSJ_-A002-20170812.pdf">optimize school bus routing for Boston Public Schools</a>, and by the MIT Robot Locomotion Group for <a href="https://juliacomputing.com/case-studies/mit-robotics.html">robot navigation and movement</a>.</p>
<p>Julia is the only high-level dynamic programming language in the “<a href="https://www.hpcwire.com/off-the-wire/julia-joins-petaflop-club/">petaflop club</a>,” having achieved 1.5 petaflop/s using 1.3 million threads, 650,000&nbsp;cores and 9,300 Knights Landing (KNL) nodes to catalogue 188 million stars, galaxies, and other astronomical objects <a href="https://juliacomputing.com/case-studies/celeste.html">in 14.6 minutes</a> on the world’s sixth-most powerful supercomputer.</p>
<p>Julia is also used to power <a href="https://juliacomputing.com/case-studies/barc.html">self-driving cars</a> and<a href="https://juliacomputing.com/case-studies/voxel8.html"> 3-D printers</a>, as well as applications in precision medicine, augmented reality, genomics, machine learning,&nbsp;and risk management.</p>
<p>“The release of Julia 1.0 signals that Julia is now ready to change the technical world by combining the high-level productivity and ease of use of Python and R with the lightning-fast speed of C++,”&nbsp;Edelman says.&nbsp;</p>
Julia 1.0, developed and incubated at MIT, was officially released to the public during JuliaCon, an annual conference of Julia users held recently in London.School of Science, Mathematics, Computer science and Artificial Intelligence Lab (CSAIL), Programming languages, Software, Computer science and technology, Artificial intelligence, Machine learning, School of Engineering, Lincoln Laboratory3 Questions: Sasha Costanza-Chock on new “#MoreThanCode” reporthttps://news.mit.edu/2018/3-questions-sasha-costanza-chock-new-morethancode-report-0824
Study of 188 practitioners distills key recommendations about using technology to advance social justice and the public interest.Thu, 23 Aug 2018 23:59:59 -0400Peter Dizikes | MIT News Officehttps://news.mit.edu/2018/3-questions-sasha-costanza-chock-new-morethancode-report-0824<p><em>Not every technology platform or tool you use, or website you visit, comes straight from a startup or Silicon Valley. Many are developed by nonprofits, government agencies, or advocacy groups practicing community technology, technology for social justice, or “public interest technology.” What can we learn from these community-engaged technology practitioners? How can organizations that work for equity achieve the diversity they often advocate for in society? </em></p>
<p><em>Sasha Costanza-Chock, an associate professor in Comparative Media Studies/Writing at MIT, is the lead author of a new report, titled “<a href="http://morethancode.cc">#MoreThanCode: Practitioners reimagine the landscape of technology for justice and equity</a>,” which delves into these issues. The report distills 109 interviews, 11 focus groups, and data from thousands of organizations into five high-level recommendations for those who want to use technology for the public good. (The report was funded by NetGain, the Ford Foundation, Mozilla, Code for America, and OTI.) </em>MIT News<em> sat down with Costanza-Chock to talk about the report and its recommendations.</em></p>
<p><strong>Q:</strong> Who are the practitioners in this tech ecosystem?</p>
<p><strong>A:</strong> “#MoreThanCode” is a report about people working to use technology for social good and for social justice — the space the report’s funders call “public interest technology.” There’s a very wide range of roles for people who use technology to advance the public interest, and it’s not only software developers who are active.</p>
<p>One of our key recommendations is that when funders and organzations — be they city governments or nonprofits or for-profit companies — are putting together teams, they need to think broadly about who is on that team. We found that a good team to develop technology that’s going to advance social justice or the public interest is going to include software developers, graphic designers, researchers, and domain [subject] experts. Domain experts might have formal expertise, but the most important team member is someone with lived experience of the particular condition that technology is supposed to address.</p>
<p><strong>Q:</strong> On that note, can you say a little about the current state of social diversity in this sector?</p>
<p><strong>A:</strong> Certainly. One of our key goals in the report was to produce baseline knowledge about who’s working in public interest technology. And unfortunately, in terms of hard data, the main finding is that we don’t have it, because many organizations in the space have not published diversity and inclusivity data about who their staff are, who their volunteers are.</p>
<p>And so one recommendation in the report is that everybody who says they’re doing public interest technology, or using technology for good, should be gathering data about, at the very least, race and gender, and publicly releasing it. Gathering and releasing diversity data, and setting time-bound, public targets for diversity and inclusion goals, are two main things that we know work in organizations, from the evidence-based literature. Good intentions aren’t enough.</p>
<p>Although we weren’t able to gather that kind of sector-wide diversity data, we did interview 109 people and conduct focus groups with 79 more, and asked them about their experiences with racism, sexism, transphobia, ableism, and other common forms of systematic marginalization people experience. About half of the people we talked to for the report said they had experiences like that.</p>
<p>The leading recommendation at the end of the report is summed up in a slogan from the disability justice movement, which is, “Nothing about us, without us.” The idea is that when you’re going to develop a technology to help a community, you have to include members of that community from the beginning of your process … and ideally in the governance of the project when it’s deployed.</p>
<p><strong>Q:</strong> The report also suggests people should not always look for “silver bullets” or instant answers from technology alone. Why is that, and what are some of the other recommendations from the report?</p>
<p><strong>A:</strong> I’m not going to say it’s never about finding a new [technological] solution, but over and over again, the people we interviewed said the projects that were most successful were deployments of resilient, proven technology, rather than some super-exciting new app that’s suddenly supposed to solve everything.</p>
<p>One recommendation is that when organizations set up tech teams, you want someone from the community on the design team, not just at a moment of consultation. That’s a pretty important takeaway. A lot of people told us it was important to go further than just doing initial consultations with a community — having people on the design team from beginning to end is a best practice we recommend.</p>
<p>Some people talked about creating tech clinics, modeled after legal clinics in education. That would be something a place like MIT could think about. Law schools often require students to spend a certain number of hours providing legal services pro bono to people in different domains who otherwise can’t afford lawyers. It would be interesting to consider whether there could be a [similar] tech clinic concept.</p>
<p>Our final recommendation was about recognizing organizational models beyond traditional startups, government offices, or 501c3 nonprofits — for example, consider tech cooperatives, or ad hoc networks that emerge around a crisis moment. These are hard for investors or foundations to fund: Whom do you fund? And yet a lot of really important technology projects are informal. In the wake of Hurricane Maria in Puerto Rico, there were hundreds of developers, techies, and community organizers doing everything they could, ad hoc, to get communications infrastructure back up.</p>
<p>People should develop strategies for supporting those kinds of networks when they do spring up. For funders, that may mean setting up a crisis response fund with a mechanism to rapidly dispense smaller amounts of funds. And members of the MIT community who are creating new companies to bring “tech for good” innovations to market should consider worker-owned cooperatives, platform co-ops, and other models that internally mirror the kind of world they’d like to build.</p>
<p><em>The report can be accessed at <a href="http://morethancode.cc">http://morethancode.cc</a>. </em></p>
Sasha Costanza-Chock Image: Allegra BovermanDiversity and inclusion, Comparative Media Studies/Writing, Computer science and technology, Politics, Social media, Social networks, Social justice, Technology and society, Social sciences, School of Humanities Arts and Social SciencesA &quot;GPS for inside your body&quot;https://news.mit.edu/2018/gps-inside-your-body-0820
CSAIL wireless system suggests future where doctors could implant sensors to track tumors or even dispense drugs.Mon, 20 Aug 2018 00:00:00 -0400Adam Conner-Simons | Rachel Gordon | CSAILhttps://news.mit.edu/2018/gps-inside-your-body-0820<p>Investigating inside the human body often requires cutting open a patient or swallowing long tubes with built-in cameras. But what if physicians&nbsp;could get a better glimpse in a&nbsp;less expensive, invasive, and time-consuming manner?</p>
<p>A team from MIT’s <a href="http://csail.mit.edu">Computer Science and Artificial Intelligence Laboratory</a> (CSAIL) led by Professor Dina Katabi is working on doing exactly that with an “in-body GPS" system dubbed&nbsp;ReMix. The new method can pinpoint the location of ingestible implants inside the body using low-power wireless signals. These implants could be used as&nbsp;tiny tracking devices on shifting tumors to help monitor&nbsp;their slight movements.</p>
<p>In animal tests, the team demonstrated that they can track the implants with centimeter-level accuracy. The team says that, one day, similar implants could be used to deliver drugs to specific regions in the body.</p>
<p>ReMix was developed in collaboration with researchers from Massachusetts General Hospital (MGH). The team describes the system in a paper that's being presented at this week's Association for Computing Machinery's Special Interest Group on Data Communications (SIGCOMM) conference in Budapest, Hungary.</p>
<div class="cms-placeholder-content-video"></div>
<p><strong>Tracking inside the body</strong></p>
<p>To test ReMix, Katabi’s group first implanted a small marker in animal tissues. To track its movement, the researchers used a wireless device that reflects radio signals off the patient.&nbsp;This was based on a wireless technology that the researchers previously demonstrated to detect <a href="http://news.mit.edu/2016/detecting-emotions-with-wireless-signals-0920">heart rate</a>, <a href="http://news.mit.edu/2014/could-wireless-replace-wearables">breathing</a>, and <a href="http://news.mit.edu/2017/dina-katabi-csail-team-develop-wireless-system-to-detect-walking-speeds-0501">movement</a>. A special algorithm then uses that signal to pinpoint the exact location of the marker.</p>
<p>Interestingly, the marker inside the body does not need to transmit any wireless signal. It simply reflects the signal transmitted by the wireless device outside the body. Therefore, it doesn't need a battery or any other external source of energy.</p>
<p>A key challenge in using wireless signals in this way is the many competing reflections that bounce off a person's body. In fact, the signals that reflect off a person’s skin are actually 100 million times more powerful than the signals of the metal marker itself.</p>
<p>To overcome this, the team designed an approach that essentially separates the interfering skin signals from the ones they're trying to measure. They did this using a small semiconductor device, called a “diode,” that mixes signals together so the team can then filter out the skin-related signals. For example, if the skin reflects at frequencies of F1 and F2, the diode creates new combinations of those frequencies, such as F1-F2 and F1+F2. When all of the signals reflect back to the system, the system only picks up the combined frequencies, filtering out the original frequencies that came from the patient’s skin.</p>
<p>One potential application for ReMix is in proton therapy, a type of cancer treatment that involves bombarding tumors with beams of magnet-controlled protons. The approach allows doctors to prescribe higher doses of radiation, but requires a very high degree of precision, which means that it’s usually limited to only certain cancers.</p>
<p>Its success hinges on something that's actually quite unreliable: a tumor staying exactly where it is during the radiation process. If a tumor moves, then healthy areas could be exposed to the radiation. But with a small marker like ReMix’s, doctors could better determine the location of a tumor in real-time and either pause the treatment or steer the beam into the right position. (To be clear, ReMix is not yet accurate enough to be used in clinical settings. Katabi says a margin of error closer to a couple of millimeters would be necessary for actual implementation.)</p>
<p>"The ability to continuously sense inside the human body has largely been a distant dream," says Romit Roy Choudhury, a professor of electrical engineering and computer science at the University of Illinois, who was not involved in the research. "One of the roadblocks has been wireless communication to a device and its continuous localization. ReMix makes a leap in this direction by showing that the wireless component of implantable devices may no longer be the bottleneck."</p>
<p><strong>Looking ahead</strong></p>
<p>There are still many ongoing challenges for improving ReMix. The team next hopes to combine the wireless data with medical data, such as that from magnetic resonance imaging (MRI) scans, to further improve the system’s accuracy. In addition, the team will continue to reassess the algorithm and the various tradeoffs needed to account for the complexity of different bodies.</p>
<p>"We want a model that's technically feasible, while still complex enough to accurately represent the human body," says MIT PhD student Deepak Vasisht, lead author on the new paper. "If we want to use this technology on actual cancer patients one day, it will have to come from better modeling a person's physical structure."</p>
<p>The researchers say that such systems could help enable more widespread adoption of proton therapy centers. Today, there are only about 100 centers globally.</p>
<p>"One reason that [proton therapy] is so expensive is because of the cost of installing the hardware," Vasisht says. "If these systems can encourage more applications of the technology, there will be more demand, which will mean more therapy centers, and lower prices for patients."</p>
<p>Katabi and Vasisht co-wrote the paper with MIT PhD student Guo Zhang, University of Waterloo professor Omid Abari, MGH physicist Hsaio-Ming Lu, and MGH technical director Jacob Flanz.</p>
The new system was developed by a CSAIL group led by Professor Dina Katabi (pictured)Photo: Simon SimardComputer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Wireless, Health, Health care, Cancer, Sensors, ResearchMore efficient security for cloud-based machine learning https://news.mit.edu/2018/more-efficient-security-cloud-based-machine-learning-0817
Novel combination of two encryption techniques protects private data, while keeping neural networks running quickly. Fri, 17 Aug 2018 00:00:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/more-efficient-security-cloud-based-machine-learning-0817<p>A novel encryption method devised by MIT researchers secures data used in online neural networks, without dramatically slowing their runtimes. This approach holds promise for using cloud-based neural networks for medical-image analysis and other applications that use sensitive data.</p>
<p>Outsourcing machine learning is a rising trend in industry. Major tech firms have launched cloud platforms that conduct computation-heavy tasks, such as, say, running data through a convolutional neural network (CNN) for image classification. Resource-strapped small businesses and other users can upload data to those services for a fee and get back results in several hours.</p>
<p>But what if there are leaks of private data? In recent years, researchers have explored various secure-computation techniques to protect such sensitive data. But those methods have performance drawbacks that make neural network evaluation (testing and validating) sluggish — sometimes as much as million times slower —&nbsp;limiting their wider adoption.</p>
<p>In a paper presented at this week’s USENIX Security Conference, MIT researchers describe a system that blends two conventional techniques — homomorphic encryption and garbled circuits — in a way that helps the networks run orders of magnitude faster than they do with conventional approaches.</p>
<p>The researchers tested the system, called GAZELLE, on two-party image-classification tasks. A user sends encrypted image data to an online server evaluating a CNN running on GAZELLE. After this, both parties share encrypted information back and forth in order to classify the user’s image. Throughout the process, the system ensures that the server never learns any uploaded data, while the user never learns anything about the network parameters. Compared to traditional systems, however, GAZELLE ran 20 to 30 times faster than state-of-the-art models, while reducing the required network bandwidth by an order of magnitude.</p>
<p>One promising application for the system is training CNNs to diagnose diseases. Hospitals could, for instance, train a CNN to learn characteristics of certain medical conditions from magnetic resonance images (MRI) and identify those characteristics in uploaded MRIs. The hospital could make the model available in the cloud for other hospitals. But the model is trained on, and further relies on, private patient data. Because there are no efficient encryption models, this application isn’t quite ready for prime time.</p>
<p>“In this work, we show how to efficiently do this kind of secure two-party communication by combining these two techniques in a clever way,” says first author Chiraag Juvekar, a PhD student in the Department of Electrical Engineering and Computer Science (EECS). “The next step is to take real medical data and show that, even when we scale it for applications real users care about, it still provides acceptable performance.”</p>
<p>Co-authors on the paper are Vinod Vaikuntanathan, an associate professor in EECS and a member of the Computer Science and Artificial Intelligence Laboratory, and Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science.</p>
<p><strong>Maximizing performance</strong></p>
<p>CNNs process image data through multiple linear and nonlinear layers of computation. Linear layers do the complex math, called linear algebra, and assign some values to the data. At a certain threshold, the data is outputted to nonlinear layers that do some simpler computation, make decisions (such as identifying image features), and send the data to the next linear layer. The end result is an image with an assigned class, such as vehicle, animal, person, or anatomical feature.</p>
<p>Recent approaches to securing CNNs have involved applying homomorphic encryption or garbled circuits to process data throughout an entire network. These techniques are effective at securing data. “On paper, this looks like it solves the problem,” Juvekar says. But they render complex neural networks inefficient, “so you wouldn’t use them for any real-world application.”</p>
<p>Homomorphic encryption, used in cloud computing, receives and executes computation all in encrypted data, called ciphertext, and generates an encrypted result that can then be decrypted by a user. When applied to neural networks, this technique is particularly fast and efficient at computing linear algebra. However, it must introduce a little noise into the data at each layer. Over multiple layers, noise accumulates, and the computation needed to filter that noise grows increasingly complex, slowing computation speeds.</p>
<p>Garbled circuits are a form of secure two-party computation. The technique takes an input from both parties, does some computation, and sends two separate inputs to each party. In that way, the parties send data to one another, but they never see the other party’s data, only the relevant output on their side. The bandwidth needed to communicate data between parties, however, scales with computation complexity, not with the size of the input. In an online neural network, this technique works well in the nonlinear layers, where computation is minimal, but the bandwidth becomes unwieldy in math-heavy linear layers.</p>
<p>The MIT researchers, instead, combined the two techniques in a way that gets around their inefficiencies.</p>
<p>In their system, a user will upload ciphertext to a cloud-based CNN. The user must have garbled circuits technique running on their own computer. The CNN does all the computation in the linear layer, then sends the data to the nonlinear layer. At that point, the CNN and user share the data. The user does some computation on garbled circuits, and sends the data back to the CNN. By splitting and sharing the workload, the system restricts the homomorphic encryption to doing complex math one layer at a time, so data doesn’t become too noisy. It also limits the communication of the garbled circuits to just the nonlinear layers, where it performs optimally.</p>
<p>“We’re only using the techniques for where they’re most efficient,” Juvekar says.</p>
<p><strong>Secret sharing</strong></p>
<p>The final step was ensuring both homomorphic and garbled circuit layers maintained a common randomization scheme, called “secret sharing.” In this scheme, data is divided into separate parts that are given to separate parties. All parties synch their parts to reconstruct the full data.</p>
<p>In GAZELLE, when a user sends encrypted data to the cloud-based service, it’s split between both parties. Added to each share is a secret key (random numbers) that only the owning party knows. Throughout computation, each party will always have some portion of the data, plus random numbers, so it appears fully random. At the end of computation, the two parties synch their data. Only then does the user ask the cloud-based service for its secret key. The user can then subtract the secret key from all the data to get the result.</p>
<p>“At the end of the computation, we want the first party to get the classification results and the second party to get absolutely nothing,” Juvekar says. Additionally, “the first party learns nothing about the parameters of the model.”</p>
<p>“Gazelle looks like a very elegant and carefully chosen combination of two advanced cryptographic primitives, homomorphic encryption and multiparty secure computation, that have both seen tremendous progress in the last decade,” says Bryan Parno, an associate professor of computer science and electrical engineering at Carnegie Mellon University.&nbsp;“Despite these advances, each primitive still has limitations; hence the need to combine them in a clever way to achieve good performance for critical applications like machine-learning inference, and indeed, Gazelle achieves quite impressive performance gains relative to previous work in this area. In terms of security, Gazelle protects both the model and the inputs to the model from leaking to curious participants via the inference computation, which is an important aspect of the problem.”</p>
A novel encryption method devised by MIT researchers secures data used in online neural networks, without dramatically slowing their runtimes, which holds promise for medical-image analysis using cloud-based neural networks and other applications. Image: Chelsea TurnerResearch, Computer science and technology, Cyber security, Machine learning, Electrical Engineering & Computer Science (eecs), Computer Science and Artificial Intelligence Laboratory (CSAIL), School of EngineeringDesign tool reveals a product’s many possible performance tradeoffs https://news.mit.edu/2018/interactive-design-tool-product-performance-tradeoffs-0815
Users can quickly visualize designs that optimize multiple parameters at once.Wed, 15 Aug 2018 10:00:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/interactive-design-tool-product-performance-tradeoffs-0815<p>MIT researchers have developed a tool that makes it much easier and more efficient to explore the many compromises that come with designing new products.</p>
<p>Designing any product — from complex car parts down to workaday objects such as wrenches and lamp stands — is a balancing act with conflicting performance tradeoffs. Making something lightweight, for instance, may compromise its durability.</p>
<p>To navigate these tradeoffs, engineers use computer-aided design (CAD) programs to iteratively modify design parameters — say, height, length, and radius of a product — and simulate the results for performance objectives to meet specific needs, such as weight, balance, and durability.</p>
<p>But these programs require users to modify designs and simulate the results for only one performance objective at a time. As products usually must meet multiple, conflicting performance objectives, this process becomes very time-consuming.</p>
<p>In a paper presented at this week’s SIGGRAPH conference, researchers from the Computer Science and Artificial Intelligence Laboratory (CSAIL) describe a visualization tool for CAD that, for the first time, lets users instead interactively explore all designs that best fit multiple, often-conflicting performance tradeoffs, in real time.</p>
<p>The tool first calculates optimal designs for three performance objectives in a precomputation step. It then maps all those designs as color-coded patches on a triangular graph. Users can move a cursor in and around the patches to prioritize one performance objective or another. As the cursor moves, 3-D designs appear that are optimized for that exact spot on the graph.</p>
<p>“Now you can explore the landscape of multiple performance compromises efficiently and interactively, which is something that didn’t exist before,” says Adriana Schulz, a CSAIL postdoc and first author on the paper.</p>
<p>Co-authors on the paper are Harrison Wang, a graduate student in mechanical engineering; Eitan Grinspun, an associate professor of computer science at Columbia University; Justin Solomon, an assistant professor in electrical engineering and computer science; and Wojciech Matusik, an associate professor in electrical engineering and computer science.</p>
<p>The new work builds off a tool, InstantCAD, <a href="http://news.mit.edu/2017/reshaping-computer-aided-design-instantcad-0724">developed last year</a> by Schulz, Matusik, Grinspun, and other researchers. That tool let users interactively modify product designs and get real-time information on performance. The researchers estimated that tool could reduce the time of some steps in designing complex products to seconds or minutes, instead of hours.</p>
<p>However, a user still had to explore all designs to find one that satisfied all performance tradeoffs, which was time-consuming. This new tool represents “an inverse,” Schulz says: “We’re directly editing the performance space and providing real-time feedback on the designs that give you the best performance. A product may have 100 design parameters … but we really only care about how it behaves in the physical world.”</p>
<p>In the new paper, the researchers home in on a critical aspect of performance called the “Pareto front,” a set of designs optimized for all given performance objectives, where any design change that improves one objective worsens another objective. This front is usually represented in CAD and other software as a point cloud (dozens or hundreds of dots in a multidimensional graph), where each point is a separate design. For instance, one point may represent a wrench optimized for greater torque and less mass, while a nearby point will represent a design with slightly less torque, but more mass.</p>
<p>Engineers laboriously modify designs in CAD to find these Pareto-optimized designs, using a fair amount of guesswork. Then they use the front’s visual representation as a guideline to find a product that meets a specific performance, considering the various compromises.</p>
<p>The researchers’ tool, instead, rapidly finds the entire Pareto front and turns it into an interactive map. Inputted into the model is a product with design parameters, and information about how those parameters correspond to specific performance objectives.</p>
<p>The model first quickly uncovers one design on the Pareto front. Then, it uses some approximation calculations to discover tiny variations in that design. After doing that a few times, it captures all designs on the Pareto front. Those designs are mapped as colored patches on a triangular graph, where each patch represents one Pareto-optimal design, surrounded by its slight variations. Each edge of the graph is labeled with a separate performance objective based on the input data.</p>
<p>In their paper, the researchers tested their tool on various products, including a wrench, bike frame component, and brake hub, each with three or four design parameters, as well as a standing lamp with 21 design parameters.</p>
<p>With the lamp, for example, all 21 parameters relate to the thickness of the lamp’s base, height and orientation of its stand, and length and orientation of three elbowed beams attached to the top that hold the light bulbs. The system generated designs and variations corresponding to more than 50 colored patches reflecting a combination of three performance objectives: focal distance, stability, and mass. Placing the cursor on a patch closer to, say, focal distance and stability generates a design with a taller, straighter stand and longer beams oriented for balance. Moving the cursor farther from focal distance and toward mass and stability generates a design with thicker base and a shorter stand and beams, tilted at different angles.</p>
<p>Some designs change quite dramatically around the same region of performance tradeoffs and even within the same cluster. This is important from an engineer’s perspective, Schulz says. “You’re finding two designs that, even though they’re very different, they behave in similar ways,” she says. Engineers can use that information “to find designs that are actually better to meet specific use cases.”</p>
<p>“This work is an important contribution to interactive design of functional real-world objects,” says Takeo Igarashi, a professor of computer science at the University of Tokyo, and an expert in graphic design. Existing computational design tools, Igarashi says, make it difficult for designers to explore design trade-offs. “The tools work as black box and allow no or limited user control,” he says. “This work explicitly addresses this not-yet-tackled important problem. … [It] builds on a solid technical foundation, and the ideas and techniques in this paper will influence the design of design tools in the future.”</p>
<p>The work was supported by the Defense Advanced Research Projects Agency, the Army Research Office, the Skoltech-MIT Next Generation Program, and the National Science Foundation.</p>
CSAIL researchers have developed a visualization tool for CAD that, for the first time, lets users instead interactively explore all designs that best fit multiple, often-conflicting performance tradeoffs, in real time.Courtesy of the researchersResearch, Design, Manufacturing, Algorithms, Computer science and technology, Software, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering, National Science Foundation (NSF)Novel optics for ultrafast cameras create new possibilities for imaginghttps://news.mit.edu/2018/novel-optics-ultrafast-cameras-create-new-possibilities-imaging-0813
Technique can capture a scene at multiple depths with one shutter click — no zoom lens needed.Mon, 13 Aug 2018 11:00:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/novel-optics-ultrafast-cameras-create-new-possibilities-imaging-0813<p>MIT researchers have developed novel photography optics that capture images based on the timing of reflecting light inside the optics, instead of the traditional approach that relies on the arrangement of optical components. These new principles, the researchers say, open doors to new capabilities for time- or depth-sensitive cameras, which are not possible with conventional photography optics.</p>
<p>Specifically, the researchers designed new optics for an ultrafast sensor called a streak camera that resolves images from ultrashort pulses of light. Streak cameras and other ultrafast cameras have been used to make a trillion-frame-per-second video, <a href="http://news.mit.edu/2016/computational-imaging-method-reads-closed-books-0909">scan through closed books,</a> and provide depth map of a 3-D scene, among other applications. Such cameras have relied on conventional optics, which have various design constraints. For example, a lens with a given focal length, measured in millimeters or centimeters, has to sit at a distance from an imaging sensor equal to or greater than that focal length to capture an image. This basically means the lenses must be very long.</p>
<p>In a paper published in this week’s <em>Nature Photonics</em>, MIT Media Lab researchers describe a technique that makes a light signal reflect back and forth off carefully positioned mirrors inside the lens system. A fast imaging sensor captures a separate image at each reflection time. The result is a sequence of images — each corresponding to a different point in time, and to a different distance from the lens. Each image can be accessed at its specific time. The researchers have coined this technique “time-folded optics.”</p>
<p>“When you have a fast sensor camera, to resolve light passing through optics, you can trade time for space,” says Barmak Heshmat, first author on the paper. “That’s the core concept of time folding. … You look at the optic at the right time, and that time is equal to looking at it in the right distance. You can then arrange optics in new ways that have capabilities that were not possible before.”</p>
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<p>The new optics architecture includes a set of semireflective parallel mirrors that reduce, or “fold,” the focal length every time the light reflects between the mirrors. By placing the set of mirrors between the lens and sensor, the researchers condensed the distance of optics arrangement by an order of magnitude while still capturing an image of the scene.</p>
<p>In their study, the researchers demonstrate three uses for time-folded optics for ultrafast cameras and other depth-sensitive imaging devices. These cameras, also called “<a href="http://news.mit.edu/2017/new-depth-sensors-could-be-sensitive-enough-self-driving-cars-1222">time-of-flight</a>” cameras, measure the time that it takes for a pulse of light to reflect off a scene and return to a sensor, to estimate the depth of the 3-D scene.</p>
<p>Co-authors on the paper are Matthew Tancik, a graduate student in the MIT Computer Science and Artificial Intelligence Laboratory; Guy Satat, a PhD student in the Camera Culture Group at the Media Lab; and Ramesh Raskar, an associate professor of media arts and sciences and director of the Camera Culture Group.</p>
<p><strong>Folding the optical path into time</strong></p>
<p>The researchers’ system consists of a component that projects a femtosecond (quadrillionth of a second) laser pulse into a scene to illuminate target objects. Traditional photography optics change the shape of the light signal as it travels through the curved glasses. This shape change creates an image on the sensor. But, with the researchers’ optics, instead of heading right to the sensor, the signal first bounces back and forth between mirrors precisely arranged to trap and reflect light. Each one of these reflections is called a “round trip.” At each round trip, some light is captured by the sensor programed to image at a specific time interval — for example, a 1-nanosecond snapshot every 30 nanoseconds.</p>
<p>A key innovation is that each round trip of light moves the focal point — where a sensor is positioned to capture an image — closer to the lens. This allows the lens to be drastically condensed. Say a streak camera wants to capture an image with the long focal length of a traditional lens. With time-folded optics, the first round-trip pulls the focal point about double the length of the set of mirrors closer to the lens, and each subsequent round trip brings the focal point closer and closer still. Depending on the number of round trips, a sensor can then be placed very near the lens.</p>
<p>By placing the sensor at a precise focal point, determined by total round trips, the camera can capture a sharp final image, as well as different stages of the light signal, each coded at a different time, as the signal changes shape to produce the image. (The first few shots will be blurry, but after several round trips the target object will come into focus.)</p>
<p>In their paper, the researchers demonstrate this by imaging a femtosecond light pulse through a mask engraved with “MIT,” set 53 centimeters away from the lens aperture. To capture the image, the traditional 20-centimeter focal length lens would have to sit around 32 centimeters away from the sensor. The time-folded optics, however, pulled the image into focus after five round trips, with only a 3.1-centimeter lens-sensor distance.</p>
<p>This could be useful, Heshmat says, in designing more compact telescope lenses that capture, say, ultrafast signals from space, or for designing smaller and lighter lenses for satellites to image the surface of the ground.</p>
<p><strong>Multizoom and multicolor</strong></p>
<p>The researchers next imaged two patterns spaced about 50 centimeters apart from each other, but each within line of sight of the camera. An “X” pattern was 55 centimeters from the lens, and a “II” pattern was 4 centimeters from the lens. By precisely rearranging the optics — in part, by placing the lens in between the two mirrors — they shaped the light in a way that each round trip created a new magnification in a single image acquisition. In that way, it’s as if the camera zooms in with each round trip. When they shot the laser into the scene, the result was two separate, focused images, created in one shot — the X pattern captured on the first round trip, and the II pattern captured on the second round trip.</p>
<p>The researchers then demonstrated an ultrafast multispectral (or multicolor) camera. They designed two color-reflecting mirrors and a broadband mirror — one tuned to reflect one color, set closer to the lens, and one tuned to reflect a second color, set farther back from the lens. They imaged a mask with an “A” and “B,” with the A illuminated the second color and the B illuminated the first color, both for a few tenths of a picosecond.</p>
<p>When the light traveled into the camera, wavelengths of the first color immediately reflected back and forth in the first cavity, and the time was clocked by the sensor. Wavelengths of the second color, however, passed through the first cavity, into the second, slightly delaying their time to the sensor. Because the researchers knew which wavelength would hit the sensor at which time, they then overlaid the respective colors onto the image —&nbsp;the first wavelength was the first color, and the second was the second color. This could be used in depth-sensing cameras, which currently only record infrared, Heshmat says.</p>
<p>One key feature of the paper, Heshmat says, is it opens doors for many different optics designs by tweaking the cavity spacing, or by using different types of cavities, sensors, and lenses. “The core message is that when you have a camera that is fast, or has a depth sensor, you don’t need to design optics the way you did for old cameras. You can do much more with the optics by looking at them at the right time,” Heshmat says.</p>
<p>This work “exploits the time dimension to achieve new functionalities in ultrafast cameras that utilize pulsed laser illumination. This opens up a new way to design imaging systems,” says Bahram Jalali, director of the Photonics Laboratory and a professor of electrical and computer engineering at the University of California at Berkeley.&nbsp;“Ultrafast imaging makes it possible to see through diffusive media, such as tissue, and this work hold promise for improving medical imaging in particular for intraoperative microscopes.”</p>
MIT researchers have developed novel photography optics, dubbed “time-folded optics,” that captures images based on the timing of reflecting light inside the lens, instead of the traditional approach that relies on the arrangement of optical components. The invention opens doors for new capabilities for ultrafast time- or depth-sensitive cameras.Courtesy of the researchersResearch, Sensors, Imaging, Computer science and technology, Media Lab, School of Architecture and Planning, Light3Q: Muriel Médard on the world-altering rise of 5G https://news.mit.edu/2018/3q-mit-muriel-medard-world-altering-rise-5g-wireless-0810
“The reason 5G is so different is that what exactly it will look like is still up in the air. Everyone agrees the phrase is a bit of a catch-all.”
Fri, 10 Aug 2018 16:35:01 -0400Meg Murphy | School of Engineeringhttps://news.mit.edu/2018/3q-mit-muriel-medard-world-altering-rise-5g-wireless-0810<p><em>The rise of 5G, or fifth generation, mobile technologies is refashioning the wireless communications and networking industry. The School of Engineering recently asked Muriel Médard, the Cecil H. Green Professor in the Electrical Engineering and Computer Science Department at MIT, to explain what that means and why it matters.</em></p>
<p><em>Médard, the co-founder of three companies to commercialize network coding — CodeOn, Steinwurf and Chocolate Cloud — is considered a global technology leader. Her work in network coding, hardware implementation, and her original algorithms have received widespread recognition and awards. At MIT, Médard leads the Network Coding and Reliable Communications Group at the Research Laboratory for Electronics.</em></p>
<p><strong>Q. </strong>People are hearing that 5G will transform industries across the world and bring advances in smart transportation, health care, wearables, augmented reality, and the internet of things. The media report that strategic players in the U.S. and internationally are developing these technologies for market by 2020 or earlier. What sets this generation apart from its predecessors?</p>
<p><strong>A.</strong> The reason 5G is so different is that what exactly it will look like is still up in the air. Everyone agrees the phrase is a bit of a catch-all. I’ll give you some big brush strokes on 5G and what people are looking at actively in the area.</p>
<p>In second, third, and fourth generations, people got a phone service that by 4G really became a system of phone plus data. It was all fairly traditional. For instance, people are used to switching manually from their cellular provider to available Wi-Fi at their local coffee shop or wherever.</p>
<p>One of the main ideas behind 5G is that you’ll have a single network that allows a blended offering. People are looking at using a multi-path approach, which means drawing on Wi-Fi and non-Wi-Fi 5G (or sometimes 4G) seamlessly. This poses some difficult coordination problems. It requires network coding, by using algebraic combinations, across different paths to create a single, smooth experience.</p>
<p>Another important part of 5G is that people are looking at using millimeter waves, which occupy frequencies that are high enough to avoid interference among multiple senders that are transmitting simultaneously in fairly close proximity relative to what is possible now. These high frequencies, with wide open spectrum regions, may be well-suited for very large amounts of data that need to be transmitted over fairly short distances.</p>
<p>There is also what people call “the fog,” which is something more than just how people feel in the morning before coffee. Fog computing, in effect, involves extending cloud capabilities, such as compute, storage and networking services, through various nodes and IoT gateways. It involves being able to draw on the presence of different users nearby in order to establish small, lightweight, rapidly set-up, rapidly torn-down, peer-to-peer type networks. Again, the right coding is extremely important so that we don't have difficult problems of coordination. You must be able to code across the different users and the different portions of the network.</p>
<p><strong>Q. </strong>You’ve described 5G as actively looking at incorporating services and modes of communications that have not been part of traditional offerings. What else sets it apart?</p>
<p><strong>A.</strong> Let’s talk about global reach. With 5G, people are looking at incorporating features, such as satellite service, that are seamlessly integrated with terrestrial service. For this, we also really need reliance on coding. You can imagine how there is no way you can rely on traditional coordination and scheduling across satellites and nodes on the ground on large scale.</p>
<p>Another thing that makes 5G so different from other evolutions is the sheer volume of players. If you were talking about 3G or 4G, it was pretty straightforward. Your key players were doing equipment provisioning to service providers.</p>
<p>Now it’s a very busy and more varied set of players. The different aspects that I’ve talked about are often not all considered by the same player. Some people are looking at worldwide coverage via satellite networking. Other people are looking at blending new channels, such as the millimeter wave ones I referred to earlier, with Wi-Fi, which basically requires marrying existing infrastructure with new ones.</p>
<p>I think finding a coherent and central source of information is a big challenge. You have the organization that governs cellular standards, 3GPP, but the whole industry is transforming as we watch in the area of 5G. It’s not clear whether it’s going to be 3GPP still calling the shots. You have so many new entrants that are not necessarily part of the old guard.</p>
<p><strong>Q. </strong>What do you believe people will notice on a daily level with the rise of 5G?</p>
<p><strong>A.</strong> I’ll give you my vision for the future of 5G, with the caveat that we’re now moving into an area that is more a matter of opinion. I see heterogeneity as part of the design. You're going to have a network that is talking to a large and disparate set of nodes with very different purposes for very different applications. You’re going to see a view that emphasizes integration of existing and new resources over just the deployment of new resources.</p>
<p>And I think the people who are going to win in 5G may not be the same players as before. It will be the company that figures out how to provide people with a seamless experience using the different substrates in a way that is highly opportunistic. It has to be a system that integrates everything naturally because you cannot preplan the satellite beam you're going to be in, the fog network you're going to be in, and the IoT devices that are going to be around you. There is no way even to maintain or manage so much information. Everything is becoming too complex and, in effect, organic. And my view on how to do that? Network coding. That’s an opinion but it’s a strongly held one.</p>
Muriel Médard, the Cecil H. Green Professor in the Electrical Engineering and Computer Science Department at MIT, describes how 5G, or fifth generation, mobile technologies is refashioning the wireless communications and networking industry. Photo: Lillie Paquette/School of EngineeringFaculty, 3 Questions, Wireless, Technology and society, Satellites, Networks, Mobile devices, Invention, Global, electronics, Research Laboratory of Electronics, Computer science and technology, School of Engineering, Innovation and Entrepreneurship (I&E)President Reif urges “farsighted national strategy” to address China competitionhttps://news.mit.edu/2018/new-york-times-op-ed-president-reif-china-competition-0808
New York Times op-ed by MIT president says a national focus on innovation and research is more effective than only playing defense on trade practices.Wed, 08 Aug 2018 12:00:00 -0400David L. Chandler | MIT News Officehttps://news.mit.edu/2018/new-york-times-op-ed-president-reif-china-competition-0808<p>In an <a href="https://www.nytimes.com/2018/08/08/opinion/china-technology-trade-united-states.html">op-ed piece</a> published today in <em>The New York Times</em>, MIT President L. Rafael Reif urges a more farsighted response to address China’s attempts to dominate cutting-edge technologies, which have included tactics such as industrial espionage and theft of intellectual property.</p>
<p>While strong and decisive action against such practices is essential, Reif writes, it is not enough. “[I]t would be a mistake to think that an aggressive defense alone will somehow prevent China’s technological success — or ensure America’s own,” he says.</p>
<p>Rather, the most important action the U.S. can take to protect its global leadership role is to redouble its core strength in innovation, starting with ground-breaking federally funded research.</p>
<p>China has begun to do just that, in a concerted national effort, including a project called “Made in China 2025” that aims to achieve global dominance in several key areas of technology and manufacturing. Because of these ambitious initiatives by the Chinese government, Reif writes, “stopping intellectual property theft and unfair trade practices — even if fully effective — would not allow the United States to relax back into a position of unquestioned innovation leadership.”</p>
<p>Reif adds that “Unless America responds urgently and deliberately to the scale and intensity of this challenge, we should expect that, in fields from personal communications to business, health, and security, China is likely to become the world’s most advanced technological nation and the source of the most advanced technological products in not much more than a decade.”</p>
<p>However, he emphasizes that this outcome is far from inevitable. The most effective countermeasure is to harness the power of federally funded research at American universities, “rooted in a national culture of opportunity and entrepreneurship, inspired by an atmosphere of intellectual freedom, supported by the rule of law and, crucially, pushed to new creative heights by uniting brilliant talent from every sector of our society and every corner of the world.”</p>
<p>Reif concludes that “As a nation, the United States needs to change its focus from merely reacting to China’s actions to building a farsighted national strategy for sustaining American leadership in science and innovation.”</p>
MIT President L. Rafael ReifPresident L. Rafael Reif, Innovation and Entrepreneurship (I&E), Artificial intelligence, Jobs, Computer science and technology, Quantum computing, Technology and society, Government, Policy, Politics, China, International relationsHolding law-enforcement accountable for electronic surveillancehttps://news.mit.edu/2018/holding-law-enforcement-accountable-for-electronic-surveillance-audit-0808
CSAIL system encourages government transparency using cryptography on a public log of wiretap requests.Wed, 08 Aug 2018 10:00:00 -0400Adam Conner-Simons | CSAILhttps://news.mit.edu/2018/holding-law-enforcement-accountable-for-electronic-surveillance-audit-0808<p>When the FBI filed a court order in 2016 commanding Apple to unlock the iPhone of one of the shooters in a terrorist attack in San Bernandino, California, the news made headlines across the globe. Yet every day there are tens of <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2071399" target="_blank">thousands of court orders</a> asking tech companies to turn over Americans’ private data. Many of these orders never see the light of day, leaving a whole privacy-sensitive aspect of government power immune to judicial oversight and lacking in public accountability.</p>
<p>To protect the integrity of ongoing investigations, these requests require some secrecy: Companies usually aren’t allowed to inform individual users that they’re being investigated, and the court orders themselves are also temporarily hidden from the public.</p>
<p>In many cases, though, charges never actually materialize, and the sealed orders usually end up forgotten by the courts that issue them, resulting in a severe accountability deficit.</p>
<p>To address this issue, researchers from MIT’s <a href="http://csail.mit.edu" target="_blank">Computer Science and Artificial Intelligence Laboratory</a> (CSAIL) and <a href="http://ipri.mit.edu" target="_blank">Internet Policy Research Initiative</a> (IPRI) have proposed a <a href="https://eprint.iacr.org/2018/697.pdf" target="_blank">new cryptographic system</a> to improve the accountability of government surveillance while still maintaining enough confidentiality for the police to do their jobs.</p>
<p>“While certain information may need to stay secret for an investigation to be done properly, some details have to be revealed for accountability to even be possible,” says CSAIL graduate student Jonathan Frankle, one of the lead authors of a <a href="https://eprint.iacr.org/2018/697.pdf" target="_blank">new paper</a> about the system, which they’ve dubbed “AUDIT” ("Accountability of Unreleased Data for Improved Transparency"). “This work is about using modern cryptography to develop creative ways to balance these conflicting issues.”</p>
<p>Many of AUDIT’s technical methods were developed by one of its co-authors, MIT Professor Shafi Goldwasser. AUDIT is designed around a public ledger on which government officials share information about data requests. When a judge issues a secret court order or a law enforcement agency secretly requests data from a company, they have to make an iron-clad promise to make the data request public later in the form of what’s known as a “cryptographic commitment.” If the courts ultimately decide to release the data, the public can rest assured that the correct documents were released in full. If the courts decide not to, then that refusal itself will be made known.</p>
<p>AUDIT can also be used to demonstrate that actions by law-enforcement agencies are consistent with what a court order actually allows. For example, if a court order leads to the FBI going to Amazon to get records about a specific customer, AUDIT can prove that the FBI’s request is above board using a cryptographic method called “zero-knowledge proofs.” First developed in the 1980s by Goldwasser and other researchers, these proofs counterintuitively make it possible to prove that surveillance is being conducted properly without revealing any specific information about the surveillance.</p>
<p>The team's approach builds on privacy research in accountable systems led by co-author Daniel J. Weitzner, a principal research scientist at CSAIL and director of IPRI.</p>
<p>“As the volume of personal information expands, better accountability for how that information is used is essential for maintaining public trust,” says Weitzner. “We know that the public is worried about losing control over their personal data, so building technology that can improve actual accountability will help increase trust in the internet environment overall.”</p>
<p>Another element of AUDIT is that statistical information can be aggregated so that that the extent of surveillance can be studied at a larger scale. This enables the public to ask all sorts of tough questions about how their data are being shared. What kinds of cases are most likely to prompt court orders? How many judges issued more than 100 orders in the past year, or more than 10 requests to Facebook this month? Frankle says the team’s goal is to establish a set of reliable, court-issued transparency reports, to supplement the voluntary reports that companies put out.</p>
<p>“We know that the legal system struggles to keep up with the complexity of increasing sophisticated users of personal data,” says Weitzner. “Systems like AUDIT can help courts keep track of how the police conduct surveillance and assure that they are acting within the scope of the law, without impeding legitimate investigative activity.”</p>
<p>Importantly, the team developed its aggregation system using an approach called multi-party computation (MPC), which allows courts to disclose relevant information without actually revealing their internal workings or data to one another. The current state-of-the-art MPC would normally be too slow to run on the data of hundreds of federal judges across the entire court system, so the team took advantage of the court system’s natural hierarchy of lower and higher courts to design a particular variant of MPC that would scale efficiently for the federal judiciary.</p>
<p>According to Frankle, AUDIT could be applied to any process in which data must be both kept secret but also subject to public scrutiny. For example, clinical trials of new drugs often involve private information, but also require enough transparency to assure regulators and the public that proper testing protocols are being observed.</p>
<p>“It’s completely reasonable for government officials to want some level of secrecy, so that they can perform their duties without fear of interference from those who are under investigation,” Frankle says. “But that secrecy can’t be permanent. People have a right to know if their personal data has been accessed, and at a higher level, we as a public have the right to know how much surveillance is going on.”</p>
<p>Next the team plans to explore what could be done to AUDIT so that it can handle even more complex data requests - specifically, by looking at tweaking the design via software engineering. They also are exploring the possibility of partnering with specific federal judges to develop a prototype for real-world use.</p>
<p>“My hope is that, once this proof of concept becomes reality, court administrators will embrace the possibility of enhancing public oversight while preserving necessary secrecy,” says Stephen William Smith, a federal magistrate judge who has written extensively about government accountability. “Lessons learned here will undoubtedly smooth the way towards greater accountability for a broader class of secret information processes, which are a hallmark of our digital age.”</p>
<p>Frankle co-wrote the paper with Goldwasser, Weitzner, CSAIL PhD graduate Sunoo Park and undergraduate Daniel Shaar. The paper will be presented at this week’s USENIX Security conference in Baltimore. IPRI team members will also discuss related surveillance issues in more detail at upcoming workshops for both USENIX and this week’s International Cryptography Conference (Crypto 2018) in Santa Barbara.</p>
<p>The research was supported by IPRI, National Science Foundation, the Defense Advanced Research Projects Agency, and the Simons Foundation.</p>
Computer scientists from MIT and IPRI propose a new cryptographic system to improve accountability of government surveillance while maintaining enough confidentiality for the police to do their jobs.Research, Algorithms, Privacy, Policy, Government, Cyber security, Technology and society, Law, Security studies and military, Computer science and technology, Cryptography, Electrical Engineering & Computer Science (eecs), Computer Science and Artificial Intelligence Laboratory (CSAIL), School of EngineeringConstantinos Daskalakis wins prestigious Nevanlinna Prizehttps://news.mit.edu/2018/constantinos-daskalakis-wins-prestigious-nevanlinna-prize-0801
Professor of electrical engineering and computer science is honored for his contributions to theoretical computer science.Wed, 01 Aug 2018 14:10:01 -0400Adam Conner-Simons | CSAILhttps://news.mit.edu/2018/constantinos-daskalakis-wins-prestigious-nevanlinna-prize-0801<p>Constantinos (“Costis”) Daskalakis, an MIT professor in the Department of Electrical Engineering and Computer Science and principal investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL), has won the 2018 Rolf Nevanlinna Prize, one of the most prestigious international awards in mathematics.</p>
<p>Announced today at the International Conference of Mathematicians in Brazil, the prize is awarded every four years (alongside the Fields Medal) to a scientist under 40 who has made major contributions to the mathematical aspects of computer science.</p>
<p>Daskalakis was honored by the International Mathematical Union (IMU) for “transforming our understanding of the computational complexity of fundamental problems in markets, auctions, equilibria, and other economic structures.” The award comes with a monetary prize of 10,000 euros.</p>
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<p>“Costis combines amazing technical virtuosity with the rare gift of choosing to work on problems that are both fundamental and complex,” said CSAIL Director Daniela Rus. “We are all so happy to hear about this well-deserved recognition for our colleague.”</p>
<p>A native of Greece, Daskalakis received his undergraduate degree from the National Technical University of Athens and his PhD in electrical engineering and computer sciences from the University of California at Berkeley. He has previously received such honors as the 2008 ACM Doctoral Dissertation Award, the 2010 Sloan Fellowship in Computer Science, the Simo Simons Investigator Award, and the Kalai Game Theory and Computer Science Prize from the Game Theory Society.</p>
<p>Created in 1981 by the Executive Committee of the IMU, the prize is named after the Finnish mathematician Rolf Nevanlinna. The prize is awarded for outstanding contributions on the mathematical aspects of informational sciences. Recipients are invited to participate in the Heidelberg Laureate Forum, an annual networking event that also includes recipients of the ACM A.M. Turing Award, the Abel Prize, and the Fields Medal.</p>
MIT Professor Costis Daskalakis is the 2018 winner of the Nevanlinna Prize.Photo courtesy of Costis Daskalakis.Faculty, Awards, honors and fellowships, Mathematics, Game theory, Computer science and technology, Electrical Engineering & Computer Science (eecs), Computer Science and Artificial Intelligence Laboratory (CSAIL), School of Engineering, Behavioral economicsMolecular clock could greatly improve smartphone navigation https://news.mit.edu/2018/molecular-clocks-improve-smartphone-navigation-performance-0713
Novel chip keeps time using the constant, measurable rotation of molecules as a timing reference.Fri, 13 Jul 2018 11:00:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/molecular-clocks-improve-smartphone-navigation-performance-0713<p>MIT researchers have developed the first molecular clock on a chip, which uses the constant, measurable rotation of molecules — when exposed to a certain frequency of electromagnetic radiation — to keep time. The chip could one day significantly improve the accuracy and performance of navigation on smartphones and other consumer devices.</p>
<p>Today’s most accurate time-keepers are atomic clocks. These clocks rely on the steady resonance of atoms, when exposed to a specific frequency, to measure exactly one second. Several such clocks are installed in all GPS satellites. By “trilaterating” time signals broadcast from these satellites — a technique like triangulation, that uses 3-D dimensional data for positioning — your smartphone and other ground receivers can pinpoint their own location.</p>
<p>But atomic clocks are large and expensive. Your smartphone, therefore, has a much less accurate internal clock that relies on three satellite signals to navigate and can still calculate wrong locations. Errors can be reduced with corrections from additional satellite signals, if available, but this degrades the performance and speed of your navigation. When signals drop or weaken — such as in areas surrounded by signal-reflecting buildings or in tunnels — your phone primarily relies on its clock and an accelerometer to estimate your location and where you’re going.</p>
<p>Researchers from MIT’s Department of Electrical Engineering and Computer Science (EECS) and Terahertz Integrated Electronics Group have now built an on-chip clock that exposes specific molecules — not atoms&nbsp;— to an exact, ultrahigh frequency that causes them to spin. When the molecular rotations cause maximum energy absorption, a periodic output is clocked — in this case, a second. As with the resonance of atoms, this spin is reliably constant enough that it can serve as a precise timing reference. &nbsp;</p>
<p>In experiments, the molecular clock averaged an error under 1 microsecond per hour, comparable to miniature atomic clocks and 10,000 times more stable than the crystal-oscillator clocks in smartphones. Because the clock is fully electronic and doesn’t require bulky, power-hungry components used to insulate and excite the atoms, it is manufactured with the low-cost, complementary metal-oxide-semiconductor (CMOS) integrated circuit technology used to make all smartphone chips.</p>
<p>“Our vision is, in the future, you don’t need to spend a big chunk of money getting atomic clocks in most equipment. Rather, you just have a little gas cell that you attached to the corner of a chip in a smartphone, and then the whole thing is running at atomic clock-grade accuracy,” says Ruonan Han, an associate professor in EECS and co-author of a paper describing the clock, published today in <em>Nature Electronics</em>.</p>
<p>The chip-scale molecular clock can also be used for more efficient time-keeping in operations that require location precision but involve little to no GPS signal, such as underwater sensing or battlefield applications.</p>
<p>Joining Han on the paper are: Cheng Wang, a PhD student and first author; Xiang Yi, a postdoc; and graduate students James Mawdsley, Mina Kim, and Zihan Wang, all from EECS.</p>
<p>In the 1960s, scientists officially defined one second as 9,192,631,770 oscillations of radiation, which is the exact frequency it takes for cesium-133 atoms to change from a low state to high state of excitability. Because that change is constant, that exact frequency can be used as a reliable time reference of one second. Essentially, every time 9,192,631,770 oscillations occur, one second has passed.</p>
<p>Atomic clocks are systems that use that concept. They sweep a narrow band of microwave frequencies across cesium-133 atoms until a maximum number of the atoms transition to their high states —&nbsp;meaning the frequency is then at exactly 9,192,631,770 oscillations. When that happens, the system clocks a second. It continuously tests that a maximum number of those atoms are in high-energy states and, if not, adjusts the frequency to keep on track. The best atomic clocks come within one second of error every 1.4 million years.</p>
<p>In recent years, the U.S. Defense Advanced Research Projects Agency has introduced chip-scale atomic clocks. But these run about $1,000 each — too pricey for consumer devices. To shrink the scale, “we searched for different physics all together,” Han says. “We don’t probe the behavior of atoms; rather, we probe the behavior of molecules.”</p>
<p>The researchers’ chip functions similarly to an atomic clock but relies on measuring the rotation of the molecule carbonyl&nbsp;sulfide (OCS), when exposed to certain frequencies. Attached to the chip is a gas cell filled with OCS. A circuit continuously sweeps frequencies of electromagnetic waves along the cell, causing the molecules to start rotating. A receiver measures the energy of these rotations and adjusts the clock output frequency accordingly. At a frequency very close to 231.060983 gigahertz, the molecules reach peak rotation and form a sharp signal response. The researchers divided down that frequency to exactly one second, matching it with the official time from atomic clocks.</p>
<p>“The output of the system is linked to that known number — about 231 gigahertz,” Han says. “You want to correlate a quantity that is useful to you with a quantity that is physical constant, that doesn’t change. Then your quantity becomes very stable.”</p>
<p>A key challenge was designing a chip that can shoot out a 200-gigahertz signal to make a molecule rotate. Consumer device components can generally only produce a few gigahertz of signal strength. The researchers developed custom metal structures and other components that increase the efficacy of transistors, in order to shape a low-frequency input signal into a higher-frequency electromagnetic wave, while using as little power as possible. The chip consumes only 66 milliwatts of power. For comparison, common smartphone features — such as GPS, Wi-Fi, and LED lighting —can consume hundreds of milliwatts during use.</p>
<p>The chips could be used for underwater sensing, where GPS signals aren’t available, Han says. In those applications, sonic waves are shot into the ocean floor and return to a grid of underwater sensors. Inside each sensor, an attached atomic clock measures the signal delay to pinpoint the location of, say, oil under the ocean floor. The researchers’ chip could be a low-power and low-cost alternative to the atomic clocks.</p>
<p>The chip could also be used on the battlefield, Han says. Bombs are often remotely triggered on battlefields, so soldiers use equipment that suppresses all signals in the area so the bombs won’t go off. “Soldiers themselves then don’t have GPS signals anymore,” Han says. “Those are places when an accurate internal clock for local navigation becomes quite essential.”</p>
<p>Currently, the prototype needs some fine-tuning before it’s ready to reach consumer devices. The researchers currently have plans to shrink the clock even more and reduce the average power consumption to a few milliwatts, while cutting its error rate by another one or two orders of magnitude.</p>
<p>This work was supported by a National Science Foundation CAREER award, MIT Lincoln Laboratory, MIT Center of Integrated Circuits and Systems, and a Texas Instruments Fellowship.</p>
The clock transmitter chip (pink) wired to a circuit board package. Connected is a metal gas cell (right), in which a 231.061 GHz signal generated from the chip excites the rotation of carbonyl sulfide molecules. Because the peak rotation of the molecules is constant, it can be used as a reference point to keep accurate time.Courtesy of the researchersResearch, Computer science and technology, Mobile devices, Electrical Engineering & Computer Science (eecs), School of Engineering, Nanoscience and nanotechnology, National Science Foundation (NSF)Project to elucidate the structure of atomic nuclei at the femtoscalehttps://news.mit.edu/2018/project-to-elucidate-structure-of-atomic-nuclei-at-femtoscale-0706
Laboratory for Nuclear Science project selected to explore machine learning for lattice quantum chromodynamics.Fri, 06 Jul 2018 14:00:00 -0400Scott Morley | Laboratory for Nuclear Sciencehttps://news.mit.edu/2018/project-to-elucidate-structure-of-atomic-nuclei-at-femtoscale-0706<p>The Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy (DOE) Office of Science User Facility, has selected 10 data science and machine learning projects for its Aurora Early Science Program (ESP). Set to be the nation’s first exascale system upon its expected 2021 arrival, Aurora will be capable of performing a quintillion calculations per second, making it 10 times more powerful than the fastest computer that currently exists.</p>
<p>The Aurora ESP, which commenced with 10 simulation-based projects in 2017, is designed to prepare key applications, libraries, and infrastructure for the architecture and scale of the exascale supercomputer. Researchers in the Laboratory for Nuclear Science’s Center for Theoretical Physics have been awarded funding for one of the projects under the ESP. Associate professor of physics William Detmold, assistant professor of physics Phiala Shanahan, and principal research scientist Andrew Pochinsky will use new techniques developed by the group, coupling novel machine learning approaches and state-of-the-art nuclear physics tools, to study the structure of nuclei.</p>
<p>Shanahan, who began as an assistant professor at MIT this month, says that the support and early access to frontier computing that the award provides will allow the group to study the possible interactions of dark matter particles with nuclei from our fundamental understanding of particle physics for the first time, providing critical input for experimental searches aiming to unravel the mysteries of dark matter while simultaneously giving insight into fundamental particle physics.</p>
<p>“Machine learning coupled with the exascale computational power of Aurora will enable&nbsp;spectacular advances in many areas of science,”&nbsp;Detmold adds. “Combining machine learning to lattice quantum chromodynamics&nbsp;calculations of the strong interactions between the fundamental particles that make up protons and nuclei, our project&nbsp;will enable a new level of understanding of the femtoscale world.”</p>
The image is an artist’s visualization of a nucleus as studied in numerical simulations, created using DeepArt neural network visualization software.Image courtesy of the Laboratory for Nuclear Science.Research, Laboratory for Nuclear Science, Physics, Center for Theoretical Physics, Department of Energy (DoE), School of Science, Machine learning, Supercomputing, Computer science and technology, Data, FundingAn AI system for editing music in videoshttps://news.mit.edu/2018/ai-editing-music-videos-pixelplayer-csail-0705
Given a video of a musical performance, CSAIL’s deep-learning system can make individual instruments louder or softer.Thu, 05 Jul 2018 14:00:00 -0400Adam Conner-Simons | CSAILhttps://news.mit.edu/2018/ai-editing-music-videos-pixelplayer-csail-0705<p>Amateur and professional musicians alike may spend hours pouring over YouTube clips to figure out exactly how to play certain parts of their favorite songs. But what if there were a way to play a video and isolate the only instrument you wanted to hear?</p>
<p>That’s the outcome of a new AI project out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL): a deep-learning system that can look at a video of a musical performance, and isolate the sounds of specific instruments and make them louder or softer.</p>
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<p>The system, which is “self-supervised,” doesn’t require any human annotations on what the instruments are or what they sound like.</p>
<p>Trained on over 60 hours of videos, the “PixelPlayer” system can view a never-before-seen musical performance, identify specific instruments at pixel level, and extract the sounds that are associated with those instruments.</p>
<p>For example, it can take a video of a tuba and a trumpet playing the “Super Mario Brothers” theme song, and separate out the soundwaves associated with each instrument.</p>
<p>The researchers say that the ability to change the volume of individual instruments means that in the future, systems like this could potentially help engineers improve the audio quality of old concert footage. You could even imagine producers taking specific instrument parts and previewing what they would sound like with other instruments (i.e. an electric guitar swapped in for an acoustic one).</p>
<p>In a new paper, the team demonstrated that PixelPlayer can identify the sounds of more than 20 commonly seen instruments. Lead author Hang Zhao says that the system would be able to identify many more instruments if it had more training data, though it still may have trouble handling subtle differences between subclasses of instruments (such as an alto sax versus a tenor).</p>
<p>Previous efforts to separate the sources of sound have focused exclusively on audio, which often requires extensive human labeling. In contrast, PixelPlayer introduces the element of vision, which the researchers say makes human labels unnecessary, as vision provides self-supervision.</p>
<p>The system first locates the image regions that produce sounds, and then separates the input sounds into a set of components that represent the sound from each pixel.</p>
<p>“We expected a best-case scenario where we could recognize which instruments make which kinds of sounds,” says Zhao, a PhD student at CSAIL. “We were surprised that we could actually spatially locate the instruments at the pixel level. Being able to do that opens up a lot of possibilities, like being able to edit the audio of individual instruments by a single click on the video.”</p>
<p>PixelPlayer uses methods of “deep learning,” meaning that it finds patterns in data using so-called “neural networks” that have been trained on existing videos. Specifically, one neural network analyzes the visuals of the video, one analyzes the audio, and a third “synthesizer” associates specific pixels with specific soundwaves to separate the different sounds.</p>
<p>The fact that PixelPlayer uses so-called "self-supervised” deep learning means that the MIT team doesn’t explicitly understand every aspect of how it learns which instruments make which sounds.</p>
<p>However, Zhao says that he can tell that the system seems to recognize actual elements of the music. For example, certain harmonic frequencies seem to correlate to instruments like violin, while quick pulse-like patterns correspond to instruments like the xylophone.</p>
<p>Zhao says that a system like PixelPlayer could even be used on robots to better understand the environmental &nbsp;sounds that other objects make, such as animals or vehicles.</p>
<p>He co-wrote the paper with MIT professors Antonio Torralba, in the Department of Electrical Engineering and Computer Science, and Josh McDermott, in the Department of Brain and Cognitive Sciences, as well as research associate Chuang Gan, undergraduate student Andrew Rouditchenko, and PhD graduate Carl Vondrick. It was recently accepted to the European Conference on Computer Vision (ECCV), which takes place this September in Munich, Germany.</p>
A new AI system can look at an image and determine which set of pixels are responsible for making specific sets of soundwaves.Image courtesy of MIT CSAILResearch, Algorithms, Machine learning, Behavior, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer vision, Artificial intelligence, Electrical Engineering & Computer Science (eecs), School of Engineering, Computer science and technology, Brain and cognitive sciences, School of Science, AudioChip upgrade helps miniature drones navigatehttps://news.mit.edu/2018/novel-chip-upgrade-helps-miniature-drones-navigate-0620
Low-power design will allow devices as small as a honeybee to determine their location while flying.Wed, 20 Jun 2018 00:00:00 -0400Jennifer Chu | MIT News Officehttps://news.mit.edu/2018/novel-chip-upgrade-helps-miniature-drones-navigate-0620<p>Researchers at MIT, who last year <a href="http://news.mit.edu/2017/miniaturizing-brain-smart-drones-0712">designed a tiny computer chip</a> tailored to help honeybee-sized drones navigate, have now shrunk their chip design even further, in both size and power consumption.</p>
<p>The team, co-led by Vivienne Sze, associate professor in MIT's Department of Electrical Engineering and Computer Science (EECS), and Sertac Karaman, the Class of 1948 Career Development Associate Professor of Aeronautics and Astronautics, built a fully customized chip from the ground up, with a focus on reducing power consumption and size while also increasing processing speed.</p>
<p>The new computer chip, named “<a href="http://navion.mit.edu" target="_blank">Navion</a>,” which they are <a href="http://navion.mit.edu/2018_vlsi_navion.pdf" target="_blank">presenting this week</a> at the Symposia on VLSI Technology and Circuits, is just 20 square millimeters — about the size of a LEGO minifigure’s footprint — and consumes just 24 milliwatts of power, or about 1 one-thousandth the energy required to power a lightbulb.</p>
<p>Using this tiny amount of power, the chip is able to process in real-time camera images at up to 171 frames per second, as well as inertial measurements, both of which it uses to determine where it is in space. The researchers say the chip can be integrated into “nanodrones” as small as a fingernail, to help the vehicles navigate, particularly in remote or inaccessible places where global positioning satellite data is unavailable.</p>
<p>The chip design can also be run on any small robot or device that needs to navigate over long stretches of time on a limited power supply.</p>
<p>“I can imagine applying this chip to low-energy robotics, like flapping-wing vehicles the size of your fingernail, or lighter-than-air vehicles like weather balloons, that have to go for months on one battery,” says Karaman, who is a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society at MIT. “Or imagine medical devices like a little pill you swallow, that can navigate in an intelligent way on very little battery so it doesn’t overheat in your body. The chips we are building can help with all of these.”</p>
<p>Sze and Karaman’s co-authors are EECS graduate student Amr Suleiman, who is the lead author; EECS graduate student Zhengdong Zhang; and Luca Carlone, who was a research scientist during the project and is now an assistant professor in MIT’s Department of Aeronautics and Astronautics.</p>
<p><strong>A flexible chip</strong></p>
<p>In the past few years, multiple research groups have engineered miniature drones small enough to fit in the palm of your hand. Scientists envision that such tiny vehicles can fly around and snap pictures of your surroundings, like mosquito-sized photographers or surveyors, before landing back in your palm, where they can then be easily stored away.</p>
<p>But a palm-sized drone can only carry so much battery power, most of which is used to make its motors fly, leaving very little energy for other essential operations, such as navigation, and, in particular, state estimation, or a robot’s ability to determine where it is in space. &nbsp;</p>
<p>“In traditional robotics, we take existing off-the-shelf computers and implement [state estimation] algorithms on them, because we don’t usually have to worry about power consumption,” Karaman says. “But in every project that requires us to miniaturize low-power applications, we have to now think about the challenges of programming in a very different way.”</p>
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<p>In their previous work, Sze and Karaman began to address such issues by combining algorithms and hardware in a single chip. Their initial design was implemented on a field-programmable gate array, or FPGA, a commercial hardware platform that can be configured to a given application. The chip was able to perform state estimation using 2 watts of power, compared to larger, standard drones that typically require 10 to 30 watts to perform the same tasks. Still, the chip’s power consumption was greater than the total amount of power that miniature drones can typically carry, which researchers estimate to be about 100 milliwatts.</p>
<p>To shrink the chip further, in both size and power consumption, the team decided to build a chip from the ground up rather than reconfigure an existing design. “This gave us a lot more flexibility in the design of the chip,” Sze says.</p>
<p><strong>Running in the world</strong></p>
<p>To reduce the chip’s power consumption, the group came up with a design to minimize the amount of data — in the form of camera images and inertial measurements — that is stored on the chip at any given time. The design also optimizes the way this data flows across the chip.</p>
<p>“Any of the images we would’ve temporarily stored on the chip, we actually compressed so it required less memory,” says Sze, who is a member of the Research Laboratory of Electronics at MIT. The team also cut down on extraneous operations, such as the computation of zeros, which results in a zero. The researchers found a way to skip those computational steps involving any zeros in the data. “This allowed us to avoid having to process and store all those zeros, so we can cut out a lot of unnecessary storage and compute cycles, which reduces the chip size and power, and increases the processing speed of the chip,” Sze says.</p>
<p>Through their design, the team was able to reduce the chip’s memory from its previous 2 megabytes, to about 0.8 megabytes. The team tested the chip on previously collected datasets generated by drones flying through multiple environments, such as office and warehouse-type spaces.</p>
<p>“While we customized the chip for low power and high speed processing, we also made it sufficiently flexible so that it can adapt to these different environments for additional energy savings,” Sze says. “The key is finding the balance between flexibility and efficiency.” The chip can also be reconfigured to support different cameras and inertial measurement unit (IMU) sensors.</p>
<p>From these tests, the researchers found they were able to bring down the chip’s power consumption from 2 watts to 24 milliwatts, and that this was enough to power the chip to process images at 171 frames per second — a rate that was even faster than what the datasets projected.</p>
<p>The team plans to demonstrate its design by implementing its chip on a miniature race car. While a screen displays an onboard camera’s live video, the researchers also hope to show the chip determining where it is in space, in real-time, as well as the amount of power that it uses to perform this task. Eventually, the team plans to test the chip on an actual drone, and ultimately on a miniature drone.</p>
<p>This research was supported, in part, by the Air Force Office of Scientific Research, and by the National Science Foundation.</p>
A new computer chip, smaller than a U.S. dime and shown here with a quarter for scale, helps miniature drones navigate in flight.Image courtesy of the researchers.Research, Algorithms, Autonomous vehicles, Drones, Electrical Engineering & Computer Science (eecs), IDSS, Aeronautical and astronautical engineering, Energy, Robotics, Computer science and technology, Research Laboratory of Electronics, Laboratory for Information and Decision Systems (LIDS), Robots, School of Engineering, National Science Foundation (NSF)How to control robots with brainwaves and hand gestureshttps://news.mit.edu/2018/how-to-control-robots-with-brainwaves-hand-gestures-mit-csail-0620
Computer Science and Artificial Intelligence Laboratory system enables people to correct robot mistakes on multiple-choice tasks.Wed, 20 Jun 2018 00:00:00 -0400Adam Conner-Simons | CSAILhttps://news.mit.edu/2018/how-to-control-robots-with-brainwaves-hand-gestures-mit-csail-0620<p>Getting robots to do things isn’t easy: Usually, scientists have to either explicitly program them or get them to understand how humans communicate via language.</p>
<p>But what if we could control robots more intuitively, using just hand gestures and brainwaves?</p>
<p>A new system spearheaded by researchers from MIT’s <a href="http://csail.mit.edu">Computer Science and Artificial Intelligence Laboratory</a> (CSAIL) aims to do exactly that, allowing users to instantly correct robot mistakes with nothing more than brain signals and the flick of a finger.</p>
<p>Building off the team’s <a href="http://news.mit.edu/2017/brain-controlled-robots-0306" target="_self">past work</a> focused on simple binary-choice activities, the <a href="http://www.roboticsproceedings.org/rss14/p63.pdf" target="_blank">new work</a> expands the scope to multiple-choice tasks, opening up new possibilities for how human workers could manage teams of robots.</p>
<p>By monitoring brain activity, the system can detect in real-time if a person notices an error as a robot does a task. Using an interface that measures muscle activity, the person can then make hand gestures to scroll through and select the correct option for the robot to execute.</p>
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<p>The team demonstrated the system on a task in which a robot moves a power drill to one of three possible targets on the body of a mock plane. Importantly, they showed that the system works on people it’s never seen before, meaning that organizations could deploy it in real-world settings without needing to train it on users.</p>
<p>“This work combining EEG and EMG feedback enables natural human-robot interactions for a broader set of applications than we've been able to do before using only EEG feedback,” says CSAIL Director Daniela Rus, who supervised the work. “By including muscle feedback, we can use gestures to command the robot spatially, with much more nuance and specificity.”</p>
<p>PhD candidate Joseph DelPreto was lead author on a paper about the project alongside Rus, former CSAIL postdoc Andres F. Salazar-Gomez, former CSAIL research scientist Stephanie Gil, research scholar Ramin M. Hasani, and Boston University Professor <a href="http://sites.bu.edu/guentherlab/" target="_blank">Frank H. Guenther</a>. The paper will be presented at the Robotics: Science and Systems (RSS) conference taking place in Pittsburgh next week.</p>
<p>In most previous work, systems could generally only recognize brain signals when people trained themselves to “think” in very specific but arbitrary ways and when the system was trained on such signals. For instance, a human operator might have to look at different light displays that correspond to different robot tasks during a training session.</p>
<p>Not surprisingly, such approaches are difficult for people to handle reliably, especially if they work in fields like construction or navigation that already require intense concentration.</p>
<p>Meanwhile, Rus’ team harnessed the power of brain signals called “error-related potentials” (ErrPs), which researchers have found to naturally occur when people notice mistakes. If there’s an ErrP, the system stops so the user can correct it; if not, it carries on.</p>
<p>“What’s great about this approach is that there’s no need to train users to think in a prescribed way,” says DelPreto. “The machine adapts to you, and not the other way around.”</p>
<p>For the project the team used “Baxter,” a humanoid robot from Rethink Robotics. With human supervision, the robot went from choosing the correct target 70 percent of the time to more than 97 percent of the time.</p>
<p>To create the system the team harnessed the power of electroencephalography (EEG) for brain activity and electromyography (EMG) for muscle activity, putting a series of electrodes on the users’ scalp and forearm.</p>
<p>Both metrics have some individual shortcomings: EEG signals are not always reliably detectable, while EMG signals can sometimes be difficult to map to motions that are any more specific than “move left or right.” Merging the two, however, allows for more robust bio-sensing and makes it possible for the system to work on new users without training.</p>
<p>“By looking at both muscle and brain signals, we can start to pick up on a person's natural gestures along with their snap decisions about whether something is going wrong,” says DelPreto. “This helps make communicating with a robot more like communicating with another person.”</p>
<p>The team says that they could imagine the system one day being useful for the elderly, or workers with language disorders or limited mobility.</p>
<p>“We’d like to move away from a world where people have to adapt to the constraints of machines,” says Rus. “Approaches like this show that it’s very much possible to develop robotic systems that are a more natural and intuitive extension of us.”</p>
A system developed at MIT allows a human supervisor to correct a robot's mistakes using gestures and brainwaves.Human-computer interaction, Robotics, Robots, Artificial intelligence, Wearable sensors, Neuroscience, Brain and cognitive sciences, Algorithms, Electrical Engineering & Computer Science (eecs), Computer Science and Artificial Intelligence Laboratory (CSAIL), Research, Sensors, Computer science and technology, School of EngineeringFaster analysis of medical imageshttps://news.mit.edu/2018/faster-analysis-of-medical-images-0618
Algorithm makes the process of comparing 3-D scans up to 1,000 times faster.Mon, 18 Jun 2018 00:00:00 -0400Rob Matheson | MIT News Officehttps://news.mit.edu/2018/faster-analysis-of-medical-images-0618<p>Medical image registration is a common technique that involves overlaying two images, such as magnetic resonance imaging (MRI) scans, to compare and analyze anatomical differences in great detail. If a patient has a brain tumor, for instance, doctors can overlap a brain scan from several months ago onto a more recent scan to analyze small changes in the tumor’s progress.</p>
<p>This process, however, can often take two hours or more, as traditional systems meticulously align each of potentially a million pixels in the combined scans. In a pair of upcoming conference papers, MIT researchers describe a machine-learning algorithm that can register brain scans and other 3-D images more than 1,000 times more quickly using novel learning techniques.</p>
<p>The algorithm works by “learning” while registering thousands of pairs of images. In doing so, it acquires information about how to align images and estimates some optimal alignment parameters. After training, it uses those parameters to map all pixels of one image to another, all at once. This reduces registration time to a minute or two using a normal computer, or less than a second using a GPU with comparable accuracy to state-of-the-art systems.</p>
<p>“The tasks of aligning a brain MRI shouldn’t be that different when you’re aligning one pair of brain MRIs or another,” says co-author on both papers Guha Balakrishnan, a graduate student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Department of Engineering and Computer Science (EECS). “There is information you should be able to carry over in how you do the alignment. If you’re able to learn something from previous image registration, you can do a new task much faster and with the same accuracy.”</p>
<p>The papers are being presented at the Conference on Computer Vision and Pattern Recognition (CVPR), held this week, and at the Medical Image Computing and Computer Assisted Interventions Conference (MICCAI), held in September. Co-authors are: Adrian Dalca, a postdoc at Massachusetts General Hospital and CSAIL; Amy Zhao, a graduate student in CSAIL; Mert R. Sabuncu, a former CSAIL postdoc and now a professor at Cornell University; and John Guttag, the Dugald C. Jackson Professor in Electrical Engineering at MIT.</p>
<p><strong>Retaining information</strong></p>
<p>MRI scans are basically hundreds of stacked 2-D images that form massive 3-D images, called “volumes,” containing a million or more 3-D pixels, called “voxels.” Therefore, it’s very time-consuming to align all voxels in the first volume with those in the second. Moreover, scans can come from different machines and have different spatial orientations, meaning matching voxels is even more computationally complex.</p>
<p>“You have two different images of two different brains, put them on top of each other, and you start wiggling one until one fits the other. Mathematically, this optimization procedure takes a long time,” says Dalca, senior author on the CVPR paper and lead author on the MICCAI paper.</p>
<p>This process becomes particularly slow when analyzing scans from large populations. Neuroscientists analyzing variations in brain structures across hundreds of patients with a particular disease or condition, for instance, could potentially take hundreds of hours.</p>
<p>That’s because those algorithms have one major flaw: They never learn. After each registration, they dismiss all data pertaining to voxel location. “Essentially, they start from scratch given a new pair of images,” Balakrishnan says. “After 100 registrations, you should have learned something from the alignment. That’s what we leverage.”</p>
<p>The researchers’ algorithm, called “VoxelMorph,” is powered by a convolutional neural network (CNN), a machine-learning approach commonly used for image processing. These networks consist of many nodes that process image and other information across several layers of computation.</p>
<p>In the CVPR paper, the researchers trained their algorithm on 7,000 publicly available MRI brain scans and then tested it on 250 additional scans.</p>
<p>During training, brain scans were fed into the algorithm in pairs. Using a CNN and modified computation layer called a spatial transformer, the method captures similarities of voxels in one MRI scan with voxels in the other scan. In doing so, the algorithm learns information about groups of voxels — such as anatomical shapes common to both scans — which it uses to calculate optimized parameters that can be applied to any scan pair.</p>
<p>When fed two new scans, a simple mathematical “function” uses those optimized parameters to rapidly calculate the exact alignment of every voxel in both scans. In short, the algorithm’s CNN component gains all necessary information during training so that, during each new registration, the entire registration can be executed using one, easily computable function evaluation.</p>
<p>The researchers found their algorithm could accurately register all of their 250 test brain scans — those registered after the training set — within two minutes using a traditional central processing unit, and in under one second using a graphics processing unit.</p>
<p>Importantly, the algorithm is “unsupervised,” meaning it doesn’t require additional information beyond image data. Some registration algorithms incorporate CNN models but require a “ground truth,” meaning another traditional algorithm is first run to compute accurate registrations. The researchers’ algorithm maintains its accuracy without that data.</p>
<p>The MICCAI paper develops a refined VoxelMorph algorithm that “says how sure we are about each registration,” Balakrishnan says. It also guarantees the registration “smoothness,” meaning it doesn’t produce folds, holes, or general distortions in the composite image. The paper presents a mathematical model that validates the algorithm’s accuracy using something called a Dice score, a standard metric to evaluate the accuracy of overlapped images. Across 17 brain regions, the refined VoxelMorph algorithm scored the same accuracy as a commonly used state-of-the-art registration algorithm, while providing runtime and methodological improvements.</p>
<p><strong>Beyond brain scans</strong></p>
<p>The speedy algorithm has a wide range of potential applications in addition to analyzing brain scans, the researchers say. MIT colleagues, for instance, are currently running the algorithm on lung images.</p>
<p>The algorithm could also pave the way for image registration during operations. Various scans of different qualities and speeds are currently used before or during some surgeries. But those images are not registered until after the operation. When resecting a brain tumor, for instance, surgeons sometimes scan a patient’s brain before and after surgery to see if they’ve removed all the tumor. If any bit remains, they’re back in the operating room.</p>
<p>With the new algorithm, Dalca says, surgeons could potentially register scans in near real-time, getting a much clearer picture on their progress. “Today, they can’t really overlap the images during surgery, because it will take two hours, and the surgery is ongoing” he says. “However, if it only takes a second, you can imagine that it could be feasible.”</p>
<p>"There is a ton of work using existing deep learning frameworks/loss functions with little creativity or imagination. This work departs from that mass of research with a very clever formulation of nonlinear warping as a learning problem ... [where] learning takes hours, but applying the network takes seconds," says Bruce Fischl, a professor in radiology at Harvard Medical School and a neuroscientist at Massachusetts General Hospital. "This is a case where a big enough quantitative change [of image registration] — from hours to seconds — becomes a qualitative one, opening up new possibilities such as running the algorithm during a scan session while a patient is still in the scanner, enabling clinical decision making about what types of data needs to be acquired and where in the brain it should be focused without forcing the patient to come back days or weeks later."</p>
<p>Fischl adds that his lab, which develops open-source software tools for neuroimaging analysis, hopes to use the algorithm soon. "Our biggest drawback is the length of time it takes us to analyze a dataset, and by far the more computational intensive portion of that analysis is nonlinear warping, so these tools are of great interest to me," he says.</p>
MIT researchers describe a machine-learning algorithm that can register brain scans and other 3-D images more than 1,000 times more quickly using novel learning techniques.Research, Algorithms, Imaging, Machine learning, Computer science and technology, Artificial intelligence, Health care, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering, Medicine, Health sciences and technology